In contrast to the first set of objectives, this second set of objectives was more difficult for the generative model to optimise, as shown in Figure . The easiest objectives with respect to achieving similar scores to real 5-HT2A ligands were selectivity versus membrane and selectivity versus D2 . The former likely due to the number of models run leading to low overall ratios of predicted off-targets. However, as more models are added, as in the dopamine and serotonin families, the objective becomes increasingly difficult to optimise to the standard of real 5-HT2A ligands. With the final objective of selectivity versus dopamine and serotonin barely being improved throughout optimisation. It is worth noting the caveat that real 5-HT2A ligands are likely contained in the training data of the PIDGINv5 models used in these objectives, and so will receive inflated scores compared to 'active' unseen molecules (of which most de novo molecules are unseen). Although we can't know how accurate these models are prospectively, or the maximum score achievable, the scores on real 5-HT2A ligands at least provide a minimal benchmark. Moreover, the models are able to at least distinguish 95 of 126 5-HT2A molecules with selectivity over D2, despite the fact that 124 molecules have a D2 pChEMBL value of 5 or above and therefore, 93 correctly predicted selective are actually false negative predictions with respect to the D2 model at 10 µM threshold (see Figure ), which is somewhat advantageous behaviour in this case for distinguishing selective compounds. For comparison to real 5-HT2A ligands selective over D2, we extracted the de novo nearest neighbours to the five most selective 5-HT2A ligands (see Figure ). Analogues were found in the 0.3-0.6