Assume that a treasure is placed in one of M boxes according to a known distribution and that k searchers are searching for it in parallel during T rounds. We study the question of how to incentivize selfish players so that group performance would be maximized. Here, this is measured by the success probability, namely, the probability that at least one player finds the treasure. We focus on congestion policies C() that specify the reward that a player receives if it is one of players that (simultaneously) find the treasure for the first time. Our main technical contribution is proving that the exclusive policy, in which C(1) = 1 and C() = 0 for > 1, yields a price of anarchy of (1 − (1 − 1/k) k) −1 , and that this is the best possible price among all symmetric reward mechanisms. For this policy we also have an explicit description of a symmetric equilibrium, which is in some sense unique, and moreover enjoys the best success probability among all symmetric profiles. For general congestion policies, we show how to polynomially find, for any θ > 0, a symmetric multiplicative (1 + θ)(1 + C(k))-equilibrium. Together with an appropriate reward policy, a central entity can suggest players to play a particular profile at equilibrium. As our main conceptual contribution, we advocate the use of symmetric equilibria for such purposes. Besides being fair, we argue that symmetric equilibria can also become highly robust to crashes of players. Indeed, in many cases, despite the fact that some small fraction of players crash (or refuse to participate), symmetric equilibria remain efficient in terms of their group performances and, at the same time, serve as approximate equilibria. We show that this principle holds for a class of games, which we call monotonously scalable games. This applies in particular to our search game, assuming the natural sharing policy, in which C() = 1//. For the exclusive policy, this general result does not hold, but we show that the symmetric equilibrium is nevertheless robust under mild assumptions.