Active learning is an iterative process during which the acquisition of additional data for model training is guided by a machine learning model. More specifically, a machine learning model is trained on a small part of the available data, and then the model iteratively selects the most informative sample (or batch of samples) to acquire in preparation for the next cycle of model training. Recent successful applications of active learning strategies in cheminformatics include high-throughput docking as well as the ligand-based prediction of physicochemical and biological properties. In the context of SoM prediction, active learning could transform the effort of "annotating all atoms of as many compounds as possible" into the effort of selectively annotating the most informative atoms across large data sets.