The main active learning cycle for chemical space exploration is depicted in Figure . Here, the process is started by assembling a chemical library of interest and initializing the procedure by a weighted random selection of a batch of compounds for the first iteration to ensure diversity. The binding affinities of the selected ligands are evaluated in an alchemical free energy calculation procedure. These ligands together with the obtained affinity estimates form a training set for machine learning (ML) models, which, in turn, predict binding affinities for all the ligands in the chemical library. In the next iteration, another set of compounds is selected and the same steps of the cycle are repeated. This way, the training set keeps increasing, thus improving the accuracy of the ML predictions. Most of the compounds with the highest binding affinity are identified in a small number of iterations of the cycle. In the process only a small fraction of the chemical library is evaluated explicitly with the computationally expensive physics based approach, while affinities for the rest of the ligands are predicted by the ML model.