A limitation of this and other similar studies is the choice of objective function in the active learning cycles. To demonstrate the flexibility of the FEgrow package, we demonstrated four design cycles here, two optimising for predicted affinity using the gnina CNN scoring function and two including a more direct optimisation of protein-ligand contacts extracted from crystallographic fragment screens. While we do not have enough data to assess the relative merits of these scoring functions, we expect the latter to be useful where experimental structural data exists, at least as part of a multi-objective optimisation in future. As a flexible alternative to PLIP scores trained on system-dependent crystal structures, it has also been shown that transferable neural networks can be trained on the PDBbind structural database to recognise favourable protein-ligand interactions. As shown in Figure ), to address the issue of synthetic tractability of the de novo built compounds, we inserted regular queries of the Enamine REAL database into the active learning cycles. In this way, we can use the initial chemical space to train the active learning regression models, and then over time seed the chemical space with compounds that are both similar to predicted actives and purchasable. In this way, we were able to test the predictions of the active learning workflow with a turn around time of a few weeks from order to biological testing. Of the 19 designed compounds that were purchased here, three showed measurable activity, but none approached the desired levels for further progression. Nevertheless, a similarity search showed the presence of effective inhibitors in the built chemical space, and so further investigation will focus on ranking compound designs ahead of purchase, perhaps via an extra stage of physics-based free energy calculations.