• Transfer Learning, which leverages knowledge gained from one task to improve the model performance on a different, but related, task (e.g., , ) (Fig. ). In drug discovery, transfer learning is achieved by 'pre-training' a model on a large dataset (e.g., ChEMBL or ZINC ), and then 'fine-tuning' it (by additional training) on a smaller, and task-focused dataset (e.g., bioactivity on a given macromolecular target ). The pre-training approach depends on the task to be performed and on the chosen molecular representation . Pre-training enables bypassing the need of numerous labeled training data and leveraging large corpora of unlabeled molecules; this is particularly suited for molecular properties with little experimental annotations available. Transfer learning can improve model performance in smalldata regimes, especially for de novo drug design (e.g., ). However, undesired biases incurred during pre-training may persist after fine-tuning and affect the quality of the de novo designs . Overall, the efficacy of transfer learning can depend on how related the pre-training and fine-tuning datasets are (e.g., , , ), and on the chosen molecular representation and related training strategies (e.g., ). • Reinforcement Learning, whereby the actions taken by a model are steered towards promising solutions via a reward function (Fig. ). Reinforcement learning waives the need for a labeled starting dataset entirely, at the cost of requiring an 'oracle' (e.g., a machine learning model predicting a specific property) that can accurately reward specific choices.