We therefore make use of the rapidly-growing make-on-demand compound libraries as a surrogate measure of synthetic accessibility. Ideally, we might use the entire catalogue as a chemical space in which to perform the active learning. Although such an approach has been used as a one-off screen, evaluating the regression models used here soon becomes prohibitively expensive in an active learning cycle. On the other hand, highly efficient methods have been developed for similarity and substructure searches of these libraries. We therefore make use of these searches to seed the chemical space with compounds that are similar to the predicted actives at each step of the active learning cycle (Figure (C)). In this way, at the subsequent acquisition step, we enable the algorithm to pick compounds for growing and scoring that are likely to be scored highly (due to similarity with other highly scoring compounds) and available for purchase or synthesis (due to presence in on-demand libraries).