The point of game tree search is to insulate oneself from errors in the evaluation function. The standard approach is to grow a full width tree as deep as time allows, and then value the tree as if the leaf evaluations were exact. This has been effective in many games because of the computational efficiency of the alpha-beta algorithm. A Bayesian would suggest instead to train a model of one’s uncertainty. This model adds extra information in addition to the standard evaluation function. Within such a formal model, there is an optimal tree growth procedure and an optimal method of valueing the tree. We describe how to optimally value the tree, and how to approximate on line the optimal tree to search. Our tree growth procedure provably approximates the contribution of each leaf to the utility in the limit where we grow a large tree, taking explicit account of the interactions between expanding different leaves. That is to say, our procedure estimates the relevance of each leaf and iteratively expands the most relevant leaves. Our algorithms run (under reasonable assumptions) in linear time and hence except for a small constant factor, are as efficient as alphabeta.