Given the improved optimization power of AHC in combination with DF1 seen with fewer RL updates against DRD2, AHC in combination with DF1 was compared to these REINVENT results to see if improvement was consistent over 500 RL updates and for different GPCR targets (Experiment 2, Figure ). For every target, AHC in combination with DF1 (Figure ) resulted in faster and further minimization of the docking score. For reference, the 80% retrospective precision threshold was surpassed within 100 RL updates in all cases except for the particularly challenging OX1R. However, docking score plateaus for AHC in combination with DF1 in later stages of training. This plateau signals mode collapse as uniqueness drops, similar to training without a DF as shown in Figure . Interestingly, a convergence of the normalized docking score towards the minimum score threshold of the DF occurs, and uniqueness then drops for all targets (Figure ). It appears that the model learns to generate molecules with a score just below the minimum score threshold to avoid DF penalization and is thus vulnerable to mode collapse as observed without the DF (Figure ).