In drug discovery, reinforcement learning has been used mostly for de novo design , where it consists of the following phases: (1) de novo molecule design using a molecule generator (e.g., trained on ChEMBL or ZINC ), ( ) ranking of the designs via a scoring function (e.g., docking , and structural similarity ), and (3) use of the top designs as new input to the model, to bias future generations towards desirable properties. However, reinforcement learning is faced with several challenges , , e.g., related to (a) the difficulty of condensing (multiple) complex chemical properties into single scoring functions, or, (b) possible model shortcuts, where a 'loophole' in the scoring function is exploitatively capitalized upon (e.g., learning to append a single carbon atom to trivially fulfill a novelty criterion). To avoid these failure modes, caution with the used data and reward functions becomes essential . • Active learning, which selects molecules for screening over multiple iterations to expand the current dataset and, correspondingly, improves the model for the next screening round . While many factors can be tuned when performing active learning, how molecules are selected for screening is a key aspect in hit discovery . In low-data bioactivity prediction, active learning can remarkably outperform traditional machine learning approaches, leading to several fold improvement in hit retrieval . In de novo design, active learning has (to the best of our knowledge) not been extensively explored yet. Nevertheless, active learning requires both experimental and computational resources and expertise, which might increase the barriers for its adoption.