If you're still spending 20 minutes crafting the perfect Boolean string, there's something you should know: the people outperforming you aren't using them at all.

That's not to say Boolean doesn't work. It does — if you already know exactly what to search for. But that's the problem. Boolean is precise. Recruiting is messy.

What does Boolean actually miss?

Boolean search works by matching exact keywords. You build a string like:

boolean-search.txt
$ your boolean string:
("software engineer" OR "developer") AND "Java" AND "London" NOT "junior"
Result: 47 exact matches. Hundreds of good candidates missed.

This seems logical. But here's what it misses:

  • The candidate who writes "backend engineer" instead of "software engineer"
  • The one who lists "Spring Boot" and "Kotlin" but never actually says "Java"
  • The contractor who's based in London but lists "Remote" as their location
  • The career changer whose title doesn't match but whose skills are perfect

Boolean finds what you type. It doesn't find what you mean.

What does AI search actually do differently?

AI-powered search (semantic search, embeddings, vector search — different names, same idea) understands the meaning behind your query, not just the words.

Here's the core difference between the two approaches:

Boolean

  • Matches exact keywords only
  • Misses synonyms and related terms
  • Requires you to think of every variation
  • Results limited to your vocabulary

AI Search

  • Understands meaning and context
  • Finds related skills automatically
  • Plain English — describe what you need
  • Results based on actual fit

So when you search for "Java developers in London with fintech experience", AI search will also surface:

  • Someone who lists Kotlin/Spring Boot (Java ecosystem, even without saying "Java")
  • Someone at a payments company who doesn't use the word "fintech"
  • Someone in "Greater London" or specific boroughs

The shift: Instead of telling the search engine exactly how to look, you tell it what you're looking for. It figures out the "how".

What does this mean for your daily workflow?

The practical impact comes down to three things — speed, quality, and how you spend your time. Here's how each area changes:

No more brackets, operators, and OR chains. Just describe what you need: "mid-senior Python developers with cloud experience, ideally from a startup background, based in the UK." The search engine handles the rest.

This also means less training time for new recruiters. No more teaching juniors Boolean syntax. They search the way they already think.

AI matches on meaning, not keywords. It catches career changers, non-standard titles, and people who describe their work differently to how you'd search for it.

In practice, agencies switching from Boolean to AI search report finding 2-3x more relevant candidates per search — because they're no longer limited to exact keyword matches.

The average recruiter spends 30% of their day sourcing. AI search doesn't just make that faster — it makes it better. Higher quality longlists in a fraction of the time.

That's time you get back for calls, client meetings, and actually recruiting. The sourcing happens in the background while you do the work that matters.

To understand how search fits into a broader AI workflow, read about what AI agents are and how they work for recruiters.

Is Boolean completely useless now?

No. Boolean is still useful for very specific, exact-match searches — like finding everyone at a particular company or with a specific certification number. It's a precision tool.

But for the way most recruiters actually search — "find me good people for this role" — natural language AI search is just better. It's how your brain already thinks about candidates. Now the technology matches that.

If you're curious about what else has changed in the recruitment tech landscape, check out the recruiter's tech stack in 2025 vs 2026.

Common Questions

Is Boolean search still useful for recruiters?

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Yes, for very specific exact-match queries — like finding everyone at a particular company or with a specific certification. But for general sourcing ("find me good candidates for this role"), AI search is faster and finds more relevant people because it understands meaning, not just keywords.

What is semantic search in recruitment?

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Semantic search (also called vector search or embeddings-based search) understands the meaning behind your query. Searching for "Java developers in fintech" will also surface people who list Kotlin/Spring Boot or work at payments companies — even if they never use those exact words on their profile.

How much time does AI search save compared to Boolean?

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The average recruiter spends 30% of their day sourcing. AI search produces higher quality longlists in a fraction of the time, and catches candidates that Boolean misses entirely. Most agencies report cutting sourcing time by at least half while improving candidate quality.

Bottom line: You don't need to learn better Boolean. You need search that understands what you're actually looking for.