To optimize the parameters of the active learning protocol, we start with applying this scheme on a large collection of PDE2 inhibitors for which the binding affinities have been measured experimentally (Part I of the study). This allows us to replace the computationally expensive alchemical free energy calculation step with a simple lookup table of measured affinities. In doing so, in Part I of the study we are able to explore various approaches for ligand encoding, their selection procedures and the effects on the ML predictions.