The only practical way to search this space is to use search algorithms to identify interesting subspaces of manageable sizes. Historically, most work in this area as it relates to drug discovery have used evolutionary search algorithms to address this problem and such methods have also been applied to docking. The use of evolutionary algorithms in drug discovery has been reviewed by Devi et al. . Examples involving docking include work by Pegg et al. , Nicolaou et al. , and Daeyaert and Deem who all use genetic algorithms (GA) to optimise docking scores obtained by the DOCK, , Glamdock, Autodoc-Vina programs, respectively. All three methods combine predefined molecular fragments in order to help ensure that the final molecules are synthetically accessible. Very recently, Cofala et al. and Nigam et al. presented SELFIES -based (mutations only) GA approaches for optimising docking scores. Cofala et al. optimised QuickVina 2 docking scores for COVID-19 main protease (M Pro ). However, the several of the presented molecules in this study appear synthetically inaccessible. Nigam et al. optimised docking scores to 5-hydroxytryptamine receptor 1B and Cytochrome P450 2D6 by interpolating between a known binder to each target. Finally, Cieplinski et al. and Boitreaud et al. have used variational autoencoders to optimise SMINA and Autodoc-Vina docking scores for several targets. Cieplinski et al. noted difficulties in finding good binders using this approach while Boitreaud et al. achieved some success.