One might expect that random selection of ligands to construct the ML models would yield suboptimal predictions, yet, this depends on the particular objective that is set for exploring chemical space. The random approach describes well the general features of the chemi-cal library (low RMSE, high correlation). This comes as a consequence of arbitrarily selecting ligands of diverse chemistry for model training. However, such a seemingly good performance of the random selection comes with a shortcoming: few potent compounds are being selected (low percentage of Top 50 ligands). Thus, random sampling of the chemical space could be used to obtain a general description of the library, but for ligand affinity optimization a different strategy might be preferred.