While interactive work is useful for small-scale studies, we have found it useful to automate the workflow for use on high performance computing (HPC) clusters, and since publication have added an application programming interface (API) to FEgrow (Figure ). This enables us to build virtual libraries with a common core, for example, using reactionbased generative scaffold decoration with LibInvent 21 or substructure searching of compound libraries, and then rapidly build the compounds into the protein binding pocket with FEgrow. However, unless the libraries are designed using information from the binding pocket, Figure : A) Example building and scoring of a SARS-CoV-2 inhibitor 20 using the interactive FEgrow workflow. The fixed core (grey) is extended using a user-defined, flexible linker (pink) and R-group (yellow), and scored using gnina. B) Compound libraries with substructures that match the rigid core can now be automatically grown and scored, treating the rest of the molecule as fully flexible. C) Proposed active learning cycle. Compounds are grown, built in the binding pocket and scored with FEgrow. The outputs are used to train a machine learning model, which is used to select the next batch of compounds. Optionally, the chemical space can be seeded using compounds available from on-demand chemical libraries.