Finally, to investigate whether the relatively low affinity of designed compounds is due to insufficient exploration of chemical space or the empirical objective functions used to optimise molecules, we performed a retrospective analysis of the designed compound space against known binders resulting from the COVID Moonshot crowd-sourced discovery campaign. In particular, Figure shows the three most similar compounds from the active learning runs (as defined by Tanimoto similarity search between RDKit Morgan fingerprints with a radius of 3 and size of 2048) to a curated set of 292 hit compounds. Considering that our FEgrow runs took as input only a single PDB receptor structure and pyridyl fragment core, it is clear that this fragment growing and on-demand library screening approach holds promise for suggesting biologically active compounds early in hit discovery campaigns. However, further work is needed to ensure that the most promising compounds are located at the top of ranked lists for synthetic prioritisation and testing.