Meta-Learning has been used to relate the performance of algorithms and the features of the problems being tackled. The knowledge in Meta-Learning is acquired from a set of meta-examples which are generated from the empirical evaluation of the algorithms on problems in the past. In this work, Active Learning is used to reduce the number of meta-examples needed for Meta-Learning. The motivation is to select only the most relevant problems for metaexample generation, and consequently to reduce the number of empirical evaluations of the candidate algorithms. Experiments were performed in two different case studies, yielding promissing results.