Each objective was optimised by the generative model in combination with a diversity filter to penalise exploitation. As shown in Figure , each of these objectives can be improved during generative model optimisation. For reference, 3,771 real compounds with bioactivity values against 5-HT2A were extracted from ChEMBL31 and their respective scores based on the first set of objectives are also shown. Surprisingly, the most difficult objective appears to be simple optimisation of the 5-HT2A predicted probability of optimisation; however, we suspect this is largely due to the effect of the diversity filter more heavily penalising similar molecules for this relatively 'easy' task. This is corroborated by running the objective without a diversity filter (see Figure ) which results in quick maximisation of this objective, but exploitative mode collapse shortly following (which the use of a diversity filter circumvents). Overall, it appears these objectives are relatively easy to optimise numerically. As with many drug discovery campaigns, a key challenge for 5-HT2A ligands is minimising offtarget bioactivity and achieving pharmacological selectivity. In this case, particularly against dopaminergic receptors (especially the Dopamine D2 receptor, from here on just D2) bound by typical antipsychotics) which leads to extrapyramidal symptoms as serious side-effects . As a proxy for desirable selectivity profiles, we design a second set of objectives with molscore particularly utilising PIDGINv5 functionality (as with the first set of objectives a diversity filter is also used):