Interestingly, active deep learning showed an 'adaptive behaviour' in the molecular substructures that are acquired during screening. For example, aldehydes -overrepresented in ALDH1 hits (1.06%) compared to all molecules (0.18%) -were, at some point, prioritized by exploitative and mutual information-based acquisition functions (Fig. ). In turn, the enrichment of aldehydes found among hits increased accordingly (Fig. ). This indicates that relevant structure-activity relationships can be picked up along the way based on newly discovered information. This is not true for methods not based on active learning. In fact, irrelevant substructures might get prioritized purely by their presence in the starting set. For example, in the ALDH1 dataset, similarity-based acquisition started to prioritize sulphonamides (Fig. ), which are common in this dataset, but not overrepresented in hit molecules (occurring in 14.32% of hits and 12.71% of non-hits). The adaptive nature of active learning allows it to change course during screening and might prevent models to get stuck in specific areas of the chemical space. This motivates once more the adoption of active learning to explore novel chemistry, especially in combination with exploitative and mutual-information-based acquisition functions.