In this work, we show that active learning can indeed reach a level of accuracy that is competitive with leading SoM predictors while using 80% less data for training. Our active learning approach, FAME.AL, builds on FAME 3 and the observations made during its development and validation. In preparation for active learning, we have made some improvements to the data processing workflow, atomic descriptor calculation, and the machine learning procedure (Table ). More specifically, we have • transferred the FAME 3 code base from Java-based CDK 24 to RDKit 25 and CDPKit, to maximize performance and stability. As part of this effort, we re-implemented the CDK atomic descriptors used by FAME 3 in CDPKit.