The active learning approach can be combined with alchemical free energy calculations for an efficient chemical space exploration, navigating towards high affinity binders. The iterative training of machine learning models on an increasing amount of data allows to significantly reduce the number of compounds to be evaluated with the more computationally expensive methods. An active learning procedure can be tuned to capture different characteristics of the chemical library: in the current work we demonstrate how to quickly identify the most potent binders, while sacrificing the quality of the overall description of the chemical library. This objective, however, can be altered by the particular choices within the active learning loop.