, they are skewed towards the higher value (Figure .A) and match the top end of the distribution observed for a diverse set of protein-ligand complexes. As we plot the difference of ΔGQB for each pair of compounds (ΔΔGQB) vs. the experimental relative binding free energies (ΔΔGbind), we observe a trend where the largest changes in binding affinity also involve a large ΔΔGQB value (Figure .B). While there is no straight correlation, the method is surprisingly good at detecting activity cliffs: out of the 207 pairs, when setting a cut-off at ΔΔGQB < -5 kcal mol -1 , it identifies 29 possible activity cliffs; 13 of which (45%) turn out to be correct. Conveniently, there are only a few false negatives (5; 2%). We obtain a Matthew's correlation coefficient (MCC) of 0.52. For comparison, the well-known MM-GBSA scoring scheme has no predictive power (MCC = -0.13).