This paper discusses a practical framework for the semi automatic construction of evaluation functions for games. Based on a structured evaluation function representation, a procedure for exploring the feature space is presented that is able to discover new features in a computational feasible way. Besides the theoretical aspects, related practical issues such as the generation of training positions, feature selection, and weight tting in large linear systems are discussed. Finally, we present experimental results for Othello, which demonstrate the potential of the described approach.