Opponent-model (OM) search comes with two types of risk. The first type is caused by a player's imperfect knowledge of the opponent, the second type arises from low-quality evaluation functions. In this paper, we investigate the desirability of a precondition, called admissibility, that may prevent the second type of risk. We examine the results of two sets of experiments: the first set is taken from the game of LOA, and the second set from the KQKR chess endgame. The LOA experiments show that when admissibility happens to be absent, the OM results are not positive. The chess experiments demonstrate that when an admissible pair of evaluation functions is available, OM search performs better than minimax, provided that there is sufficient room to make errors. Furthermore, we conclude that the expectation 'the better the quality of the prediction of the opponent's move, the more successful OM search is' is only true if the quality of both evaluation functions is sufficiently high.