Having established that the active learning protocols tested here are able to improve the efficiency of chemical space searches with FEgrow, we turn now to prospective design of potential noncovalent SARS-CoV-2 MPro inhibitors. A wealth of computational and experimental data has been generated for this target in recent years, but here we limit ourselves to structural information that was available in the early months of the COVID-19 pandemic. In particular, as in the previous section, we consider expansion of the pyridyl fragment (PDB: 5R83) along a vector into the binding pocket containing the catalytic cysteine (Cys145). We now expand the size of the chemical space to an initial 250,000 molecules, built from the combination of supplied libraries of 500 linkers and 500 R-groups, such that full building and scoring of the space is prohibitively expensive for routine study. To address the issue of The distribution of predicted affinity increases over the first 10 active learning cycles then starts to saturate with a mean predicted pK close to 6 (micromolar affinity). Over the full run, 95% of the compounds were successfully built (assigned pK > 0) and 15% had a predicted pK > 6. For comparison, we also extracted 1000 molecules at random that contained the pyridyl substructure from the REAL database used to seed the active learning cycles.