  We consider the classical 100 Prisoner problem and its variant, involving
empty boxes, introduced by Gal and Miltersen. Unlike previous studies, here we
focus on the winning probabilities for an arbitrary number of winners and
attempts, which we call the unconstrained problem. We introduce general classes
of strategies, applicable to different settings and quantify how efficient they
are. We make use of Monte Carlo simulations, whenever analytic results are not
available, to estimate with high accuracy the probabilities of winning.
Finally, we also comment on the possible applications of our results in
understanding processes of information retrieval, such as "memory" in living
organisms.
