Searching for a close numeric match on spatial coordinates
The smart and fast solution for this class of problems is an index-backed &quot;nearest neighbor&quot; search.
For the record: if you want precise results with spatial data use PostGis and operate with geometry or geography types. Here is a starting point. And operate with ST_DWithin(). Examples:

Order by distance
PostGIS radius query

Sticking to your setup (2-D points, no PostGis), and ignoring the additional approximation error of handling spatial data in a 2-D plain, which seems negligible for the case at hand - I suggest a space-partitioned GiST index (on an expression in your case):
CREATE INDEX tbl_location_spgist_idx ON tbl USING SPGIST (point(longitude, latitude));

Why SP-Gist? See:

Perform this hours of operation query in PostgreSQL

https://www.2ndquadrant.com/en/blog/postgresql-12-implementing-k-nearest-neighbor-space-partitioned-generalized-search-tree-indexes/


To get a maximum of 10 &quot;nearest neighbors&quot; in next to no time:
SELECT *
FROM   tbl
ORDER  BY point (longitude, latitude)
      &lt;-&gt; point '(9.876543210987654321,1.234567890123456789)'
LIMIT  10;

You can then filter the ones close enough. To get a maximum of 10 closest within a maximum distance:
SELECT *, ((longitude - 9.876543210987654321) ^ 2
        + (latitude   - 1.234567890123456789) ^ 2) AS squared_dist
FROM   tbl
WHERE  ((longitude - 9.876543210987654321) ^ 2
      + (latitude  - 1.234567890123456789) ^ 2) &lt; 0.000000001  -- or whatever
ORDER  BY point (longitude, latitude)
      &lt;-&gt; point '(9.876543210987654321,1.234567890123456789)'
LIMIT  10;

db&lt;&gt;fiddle here
I use a squared distance to get nearest neighbors based on simple Pythagorean theorem. The beauty of it: the calculation is only performed on the nearest neighbors, so it's still very fast when the calculation gets more expensive - even in big tables.