We have devised an active learning approach, FAME.AL, for the prediction of the SoMs of small molecules that are characterized by the comprehensive coverage of phase 1 and phase 2 metabolic reactions, data efficiency, high performance, robustness, and speed. The approach builds on a computationally efficient re-implementation of FAME 3, which reaches competitive performance (MCC of 0.48 on holdout data) while using 80% less data for model training.