In order to investigate the performance of the active learning protocol, and the effect of machine learning hyperparameters, we built a labelled 'oracle' set of 47 K compounds using standard FEgrow input settings (see Computational Details). This is a larger set of compounds than would be typically built and scored against a target, but knowing the affinities of the full chemical space enables us to assess the performance of the active learning ap-proach. The common core was selected to be a pyridyl fragment common to several early crystal structures of the SARS-CoV-2 main protease, 3 located in the S1 pocket with a vector pointing into the enzyme active site (Figure (a)). ), unoptimised compounds at the start of a hit finding effort. Indeed, it is at this stage where the options for expansion are vast, and strategies to suggest exploration of hits are particularly valuable. Note that compounds that could not be built (for example, due to steric clashes with the protein) are arbitrarily assigned a pK of zero, so that this information can be included in the active learning model. with well-spread clusters of the highest affinity binders, potentially providing a challenging search space for active learning. Figure (b) also shows locations in the chemical space of example linker and R-groups, attached to the pyridyl core, that make up the stronger predicted binders. Favourable predicted linkers include amides, sulfonylurea and various 6-membered ring heterocycles, and relatively bulky R-groups are feasible, which is generally expected given the size and shape of the binding pocket. (Note that at this stage no consideration is given to synthetic accessibility or stability of the compound designs). We next sought to use active learning to accelerate the search through this chemical space, using the oracle to assess the performance of model hyperparameters, and using the predicted binding affinity as the optimisation target. In particular, we have investigated the effects of initial compound selection (random or diverse), number of compounds picked per cycle (in the range 200-500), machine learning model (GBM or GP) and acquisition method (greedy or UCB). As discussed, the dependence of active learning efficiency on the choice of model parameters is well documented, and so we do not devote much space to it here.