
Starting with strict filters can miss great candidates. Here's when and how to broaden your TotalJobs search to find hidden talent.
You've set the filters perfectly. Location, salary, sector, keywords. The search returns twenty candidates. You read all twenty โ properly, in three layers. The right person isn't among them.
Most recruiters respond by refreshing the same search and waiting for new profiles to appear. The smarter move is to question the filters. Because on TotalJobs, the candidate you need is usually in the database already โ sitting just outside your search.
TotalJobs matches against what candidates typed into their profiles. Your filters search their words, not their reality. That gap creates three traps:
Title drift. The same job exists under ten names. You're searching "Cloud Security Engineer". The right person calls themselves "DevSecOps Engineer", "Security Architect", or "Infrastructure Security Lead". Same work. Different label. The filter never connects them.
Stale profiles. Good candidates stop updating their profile the moment they're happily employed. Their title still says "Network Engineer" โ but their last three years were cloud security. The skills are there, buried in a role description written in 2021. Search by title and you never see them.
Phrasing. Two people do the same job and describe it differently. One writes "Azure security". Another writes "hardened Azure workloads". One matches your keywords. The other doesn't. The database can't tell you they're the same person.
The trap is comfortable because it's invisible. The search worked โ it returned results. What it didn't return doesn't show up anywhere.
Every CV you download on TotalJobs costs money. That's the visible cost โ and it makes people cautious about broadening, understandably.
But count the other side. One missed candidate can mean a role unfilled for another three weeks โ or filled by a competitor. If broadening costs you forty extra downloads and finds you one candidate who gets placed, the maths isn't close.
The discipline isn't don't spend on downloads. It's spend on the right downloads: candidates you haven't seen before, searched for deliberately.
Broadening isn't the default. It's a response to signals:
| Signal | What it means | What to do |
|---|---|---|
| Fewer than 30 results | Your filters are excluding most of the market | Broaden the title first |
| Every result is "not quite" | The right people phrase it differently | Swap keywords for the underlying skill |
| Strong CVs, wrong title | Title drift is hiding your candidates | Search adjacent titles deliberately |
| Results feel stale | Active candidates aren't updating profiles | Loosen salary band and radius slightly |
No signal, no broadening. If the strict search gives you thirty relevant CVs, stop searching and start reading. Broadening without a reason just buys you a bigger pile of the same problem.
When you do broaden, do it one step at a time โ a ladder, not a flood. Relax everything at once and you get 900 results and learn nothing about which filter was hiding your candidates.

Step 1 โ Broaden the title. The single biggest win. For "Cloud Security Engineer", search adjacent titles one at a time: DevSecOps Engineer, Security Architect, Infrastructure Security Lead, Platform Engineer (security). Each title is its own search, its own result list.
Step 2 โ Swap keywords for skills. If titles aren't finding them, search the work itself: "Azure security", "SIEM", "infrastructure hardening", "ISO 27001".
Step 3 โ Search the outcome. The strongest candidates describe what they achieved: "migrated to Azure", "built a SOC", "led a zero-trust rollout". Outcome searches surface people who do the work but never list the buzzword.
Step 4 โ Loosen location before salary. A 45-minute commute difference is a conversation, not a dealbreaker. Candidates answer ads outside their stated radius all the time. Widen by ten miles and see what appears.
Step 5 โ Loosen salary last. ยฑ10% catches candidates who've grown past their listed figure. Do this only when the other steps have run dry โ it adds the most noise.
At every step, note what the change produced. That's the next habit.
Broadened searches overlap. The wider the net, the more CVs you've already seen resurface โ and on TotalJobs, paying twice for the same CV is the one cost that's never justified.

Before each download batch:
Over a month of broadened searching, dedupe is the difference between paying for 200 CVs and paying for 140 โ with the same shortlist quality at the end.
The real skill isn't any single search. It's remembering what worked last time, so each role starts from a better place than the last.
Keep one line per search:
| Role | Filters used | Results | Best find | Lesson |
|---|---|---|---|---|
| Cloud Security Eng. | Title only, 20mi | 22 | โ | Too narrow โ most results stale |
| Cloud Security Eng. | Title + "DevSecOps" | 61 | Migration lead, energy sector | "DevSecOps" title found the market |
| Platform role | Skill "Azure security" | 84 | โ | Good, but 30 duplicates from last week |
After five roles, your log is your search strategy for your market. You'll know which titles your candidates use, which radius actually works, and which searches only produce duplicates. You won't be guessing โ you'll be compounding.
Searching strategically isn't about searching more. It's about knowing when your filters are lying to you, broadening deliberately, and never paying twice for the same CV. The candidates are already in the database โ the ones you haven't seen are the ones your filters never met.
Paste a job ad and let AI search TotalJobs, extract CVs, and build structured schemas. Stop scrolling job boards โ let AI source for you.

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