In the first part of the investigation, we explored the efficiency and convergence of the ac-tive learning protocols on a data set of PDE2 inhibitors with experimentally measured binding affinities. The collection of 2351 ligands interacting with PDE2 has been assembled in Janssen Pharmaceutica from the corresponding drug discovery project. This set presents a convenient case for probing different versions of model building protocols, directly based on experimentally measured ∆G values, rather than relying on computational methods. This way, the oracle in Figure is represented by the experimentally obtained affinities. We started by generating binding poses for the ligands relying on the 6EZF crystal structure of PDE2, followed by encoding ligand representations for machine learning. As this collection contains molecules with a variety of chemical scaffolds, ligands could not be uniquely described solely by R-groups attached to a single scaffold. Hence, only representations involving features of complete ligands were used.