Scraping Google is one of the most cost-effective ways to build a lead list. But most people hit a wall quickly because Google stops showing results after the first 250 or 300 entries.
Even if millions of pages match your search, the later pages simply return empty.
Datablist solves this limitation with the "Start with Google Search Queries" source.
Instead of running one search and getting only 200 results, you can run dozens or hundreds of variations at once.
You can get roughly 4,000 results for just $1.
This guide shows you how to automate this process to extract thousands of high-quality results without touching a single line of code.
📌 Examples of use cases
- Create a lead list with LinkedIn profiles
- Search Instagram profiles using keywords
- Target companies by specific web technologies
- Build local company databases across hundreds of cities
Quick links to sections:
- Understanding the 300-Result Barrier
- The Multi-Query Solution & using AI for Query Variation
- Step-by-Step Guide to Datablist Google Scraping
- Cleaning and Deduplicating Your Data
- Enriching Your Search Results
- Cost Analysis: Scraping on a Budget
Understanding the 300-Result Barrier
Google optimizes its engine for human browsing. Most people find what they need on page one.
Google sees no reason to serve thousands of results to a single user (and want to prevent scraping abuse...)
If you attempt to scroll to page 40, you often find a message stating Google omitted similar results, or the pages simply appear empty.
For lead generation, this is a disaster. If you search for "Lawyer UK" you might find 250 great prospects. However, thousands more remain invisible because they occupy positions 301 through 10,000. You cannot reach them with a broad keyword.
The only way to access these hidden rows is to make your search more specific. By narrowing the scope, you force Google to show the top 300 results for a smaller niche. If you repeat this process across dozens of niches, you eventually reconstruct the entire list.
The Multi-Query Solution
Multi-query scraping involves breaking a large search into many smaller, overlapping queries. Instead of searching for "Lawyers in the United States," you search for "Lawyers in New York," "Lawyers in Los Angeles," "Lawyers in Chicago," "Lawyers in Houston," and "Lawyers in Miami."
Each specific city search provides a fresh set of 200 to 300 results. If you run searches for the top 50 cities in United States, you potentially collect 15,000 results. While some overlap occurs (a lawyer might rank for two nearby cities), the unique results far exceed what a single broad search offers.
This strategy works because it changes the "intent" of the search from Google's perspective. By using geographic or other variation patterns, you move the prospects from position 5,000 in the broad search to position 1 in the specific search.
Using AI for Query Variation
Creating 100 variations of a search query manually takes time. But Modern LLMs like ChatGPT, Gemini, or Claude do it for you instantly. You can feed a prompt to an AI and receive a perfectly formatted list of search queries in seconds.
The Location Strategy
Location is the easiest way to multiply results. Every country has lists of cities, states, or regions.
I need to find dentists in Australia. Google limits me to 300 results. Please generate a list of 50 search queries following the pattern.
Use the 50 most populous cities.
Return the list of queries in a text canvas zone, one per line.
## Pattern
dentist [City Name], Australia
The Keyword Variation Strategy
Sometimes locations do not fit. If you seek "Remote Marketing Agencies," you might vary the keywords instead.
Examples: 'B2B marketing agency', 'E-commerce marketing agency', 'SaaS marketing agency', 'Real estate marketing agency'. Use diverse industries to ensure different companies appear in the results.
Return the list of queries in a text canvas zone, one per line.
The "Fingerprint" Strategy
Many websites use specific software. This software often leaves a footprint in the HTML code or footer. Google indexes this text.
Example: '“Powered by Shopify” jewelry', '“Powered by Shopify” fitness'.
Return the list of queries in a text canvas zone, one per line.
Step-by-Step Guide to Google Bulk Queries Scraping
Once you have the list of queries, Datablist makes the technical side of running them in parallel invisible. You do not need to worry about rotating proxies, handling headless browsers or extracting data from the html.
1. Access the Data Source
Open Datablist and click "Start from a data source" on the sidebar.
Look for the "Start with Google Search Queries" data source. This source is specifically designed for high-volume extraction.
2. Paste Your Queries & configures search parameters
Copy the list of variations generated by your AI. Paste them into the query field in Datablist. You can paste dozens or hundreds of lines at once.
Set the target country and language. This is vital. If you search for UK lawyers but set the country to the US, Google returns different (and likely irrelevant) results. You can also specify a time period if you only want results indexed in the last month or year.
4. Execute and Wait
Click the run button. Datablist processes the queries. Because Google scraping requires careful management to avoid blocks, the system handles the timing for you.
You can watch as the items populate your collection in real-time.
Cleaning and Deduplicating Your Data
Deduping
A major side effect of running 50 similar queries is duplicate data. A popular law firm might rank for "Lawyer London," "Lawyer UK," and "Commercial Lawyer." When you merge these results into one Datablist collection, you will have three rows for the same firm.
You must deduplicate your data before starting outreach. Datablist includes a powerful Duplicates Finder.
- Open the "Clean" menu.
- Select "Duplicates Finder."
- Choose the property to compare. For Google results, "Result Link" is the best choices.
- Select the "URL" preprocessor to ignore path, query params, etc. during the dedupe processing.
- Let the tool identify matching rows and merge them or delete the extras.
Remove noise
Cleaning also involves removing noise. Some Google results will be "aggregators" like Yelp, Yellow Pages, or Tripadvisor. You likely want to remove these to focus on direct company websites.
Use the filtering features to exclude common directory domains. You can find detailed steps on managing these files in data cleaning this guide.
Enriching Your Search Results
A URL or a page title is rarely enough for a sales campaign. Once you have a unique list of websites, you need contact information. Datablist acts as an enrichment hub where you can pipe your scraped data into other services.
Finding Emails
-
Get emails from company domains
If your scrape returns company domains, use the Datablist "Waterfall People Search" enrichment. It finds people working at those companies and returns their profile details with verified email addresses. This is ideal for building targeted B2B contact lists. -
Get emails from LinkedIn profile URLs
If your scrape returns LinkedIn profile links, use the Datablist Waterfall Email Finder. It finds the professional email address using only the LinkedIn profile URL. You can follow our step by step guide here: Find email addresses from a LinkedIn Profile URL.
Get LinkedIn Company Pages from Domains
If you start with company websites, you can turn them into LinkedIn assets in one click. Use the "LinkedIn Company Page Matcher" enrichment. It finds the official LinkedIn Company Page for each business in your list.
This is powerful. A simple Google result becomes a rich company profile with industry, size, and activity data.
Once matched, you can pull detailed company information using:
You move from raw URLs to structured B2B data fast.
AI Agent to visit websites
Sometimes a website hides the good stuff. That is where the AI Agent comes in.
The AI Agent visits each website for you. It reads the pages like a human would.
It can:
- Categorize companies based on their website content
- Label leads as "High Priority" or "Low Priority"
- Extract contact details from Contact or About Us pages
Instead of opening 500 tabs and reading them one by one, you let the agent do the heavy lifting.
Case Studies and Use Cases
Case Study: The Niche Agency
A marketing agency specializing in veterinarians wanted to expand across the United States. A single search for "veterinarian USA" yielded 300 results. By generating a list of the 500 largest US cities and running them through Datablist, they collected 85,000 raw results.
After deduplicating by domain, they had 42,000 unique veterinary clinics. They then enriched these with contact information.
Case Study: The Tech Recruiter
A recruiter needed to find CTOs of startups in Berlin. They used the query:
site:linkedin.com/in/ "CTO" "Berlin" "startup"
They created variations by changing "Berlin" to other German tech hubs like Munich, Hamburg, and Cologne. They also varied the title: "VP Engineering," "Technical Co-founder," and "Head of Development". This multi-pronged approach built a candidate pool of 4,000 executives, far exceeding what a standard LinkedIn search allows.
Case Study: Finding Online Stockists
A cosmetic brand owner wanted to find independent online boutiques to stock their new line of organic face oils. Instead of searching manually, they targeted the "Powered by Shopify" footprint, which is common among independent retailers.
They used the "Fingerprint" strategy:
"organic face oil" "powered by shopify"
By using an LLM to generate variations for every product category ("natural beauty boutique," "vegan skincare," "botanical serum"), they identified over 1,200 unique online stores matching their ideal distributor profile.
They imported the results into Datablist, removed duplicates, and used the Datablist AI Agent to find contact information.
Cost Analysis: Scraping on a Budget
Traditional web scraping is expensive. Hiring a developer to build a custom scraper often costs thousands of dollars. Using dedicated scraping APIs usually requires a monthly subscription and technical knowledge to handle the JSON outputs.
Datablist simplifies the economics. The credit-based system means you only pay for the data you successfully extract.
- Rate: 2.5 credits for every 10 Google results.
- Value: With credit packages starting at $1 for 20,000 credits, the math is simple.
- Result: You get 4,000 results for approximately $1.
Compared to buying "stale" lead lists from brokers, which can cost $0.50 per lead, scraping fresh data from Google is orders of magnitude cheaper. You control the filters, the timing, and the niches.
Tips: Use Google Search Operators
To maximize the quality of your scraped data, here are some Google search operators you can use to build your queries.
These symbols tell Google exactly where to look for your keywords.
- site: Use this to scrape results from a specific platform. To find LinkedIn profiles, use
site:linkedin.com/in/. - inurl: This looks for words within the URL. To find contact pages, use
inurl:contact. - intitle: This finds words in the page title.
intitle:"Index of"often finds open directories. - filetype: Use this for finding documents.
filetype:pdf "marketing plan"finds public strategy documents. - - (Minus sign): Exclude words. If you want lawyers but not "recruitment" agencies, use
lawyer -recruitment.
Combining these with multi-query scraping creates a surgical tool. For example:
site:instagram.com "concept store" -inurl:/p/
This search finds Instagram profile pages for concept stores while excluding individual posts (the /p/ path). Running this for 50 different countries or niches gives you a global database of influencers or competitors.
📘 Check our Search and Scrape Instagram Profiles by Category and Keywords to learn more on Instagram Profiles search using Google
FAQ
Why does Google limit results to 300?
Google aims to provide the best user experience. They assume that if you haven't found what you need by the 30th page, the subsequent pages are unlikely to help. This also protects their servers from automated bots trying to download the entire internet.
Is Google scraping legal?
Scraping publicly available data is generally legal for business purposes like research or lead generation. However, you must respect privacy laws like GDPR when handling personal data. Always check the terms of service of the specific sites you visit via the search results.
Can I scrape Google Maps with this tool?
The "Start with Google Search Queries" source focuses on the standard Search engine. If you need local business data including ratings, opening hours, and precise map coordinates, you should use the dedicated Google Maps scraper. Search results are better for websites and digital profiles; Maps results are better for physical locations.
How do I handle "CAPTCHAs"?
When using Datablist, you don't. The platform manages the request headers and proxy rotation. If a query encounters a challenge, the system handles the retry logic. You see only the finished data in your collection.
Can I use these results in my CRM?
Yes. Datablist allows you to export your cleaned and enriched data as a CSV or Excel file. Most CRMs like HubSpot, Salesforce, or Pipedrive allow you to import these files directly. By cleaning the data in Datablist first, you ensure your CRM remains free of duplicates and junk entries.