  Finding a fluorescent target in a biological environment is a common and
pressing microscopy problem. This task is formally analogous to the canonical
search problem. In ideal (noise-free, truthful) search problems, the well-known
binary search is optimal. The case of half-lies, where one of two responses to
a search query may be deceptive, introduces a richer, R\'enyi-Ulam problem and
is particularly relevant to practical microscopy. We analyse microscopy in the
contexts of R\'enyi-Ulam games and half-lies, developing a new family of
heuristics. We show the cost of insisting on verification by positive result in
search algorithms; for the zero-half-lie case bisectioning with verification
incurs a 50% penalty in the average number of queries required. The optimal
partitioning of search spaces directly following verification in the presence
of random half-lies is determined. Trisectioning with verification is shown to
be the most efficient heuristic of the family in a majority of cases.
