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We introduce potential fitness, a variant of fitness function that operates in the space of schemata and is applicable to tree-based genetic programing. The proposed evaluation algorithm estimates the maximum possible gain in fitness of an individual's direct offspring. The value of the potential fitness is calculated by analyzing the context semantics and subtree semantics for all contexts (schemata) of the evaluated tree. The key feature of the proposed approach is that a tree is rewarded for the correctly classified fitness cases, but it is not penalized for the incorrectly classified ones, provided that such errors are recoverable by substitution of an appropriate subtree (which is however not explicitly considered by the algorithm). The experimental evaluation on a set of seven boolean benchmarks shows that the use of potential fitness may lead to better convergence and higher success rate of the evolutionary run.