  We consider a generalization of the classical 100 Prisoner problem and its
variant, involving empty boxes, whereby winning probabilities for a team depend
on the number of attempts, as well as on the number of winners. We call this
the unconstrained 100 prisoner problem. After introducing the 3 main classes of
strategies, we define a variety of `hybrid' strategies and quantify their
winning-efficiency. Whenever analytic results are not available, we make use of
Monte Carlo simulations to estimate with high accuracy the
winning-probabilities. Based on the results obtained, we conjecture that all
strategies, except for the strategy maximizing the winning probability of the
classical (constrained) problem, converge to the random strategy under weak
conditions on the number of players or empty boxes. We conclude by commenting
on the possible applications of our results in understanding processes of
information retrieval, such as ``memory'' in living organisms.
