In this study, we have combined the FEgrow software, an open modular workflow for building and scoring ligands in protein binding pockets, with active learning to guide and automate chemical space searches for promising binders. In agreement with numerous other studies, we have shown that search efficiency is not too dependent on the hyperparameters of the active learning model, which include the choice of regression model, the acquisition function and number of compounds picked per cycle. For this particular study, we find efficiency improvements of a factor of around 5x over random selection, which will aid throughput of future prospective design efforts.