In this minireview, we define context-enriched training as an umbrella term encompassing approaches that provide additional knowledge to the model ('context') to improve its performance on a given task. Unlike multistage learning, in context-enriched training all available information is provided to the model simultaneously. Context-enrichment can be performed in several different ways, such as: • Multimodal training. Multimodal training leverages multiple input types (e.g., different molecular representations) to enhance the model performance on a given task . Combining molecular information from different modalities might allow the model to learn more informative representations for the task at hand, potentially improving performance compared to models that rely on a single representation. In several application domains (e.g., medical image analysis and computer vision ) multimodal learning has shown promise to alleviate the limitations of scarce data. In the molecular sciences, it has been applied to several tasks (for instance, to combine molecular graphs and textual data , and ligand and proteins graphs ), showing promise for zero-and few-shot prediction . Since each molecular representation captures only part of the underlying 'molecular reality', multimodal deep learning is particularly relevant in the molecular sciences. However, it also carries several limitations, e.g., choosing how to effectively combine modalities, and 'modality competition' , whereby only a subset of the input modalities is leveraged by the model to make a prediction. • Multi-task learning. In multi-task learning, a model is trained to predict multiple outputs in parallel (e.g., multiple molecular properties ). The underlying idea is that the model error is optimized across all tasks (with the possibility to include missing values, if necessary), encouraging learning a shared representation that is beneficial for all tasks . Although multi-task learning does not seem to systematically outperform single-task approaches for bioactivity prediction, it seems beneficial on tasks that have fewer labelled molecules . Multi-task learning also comes with some caveats, for instance: (a) Its effectiveness relies on the assumption that the tasks are related, leading to failure when tasks that are too different from each other ; (b) the unavailability of all labels for each datapoint might affect the overall performance; and (c) an 'easy' tasks to model may become dominant during the training process, to the detriment of the more difficult ones. Hence, the complexity-performance trade-off of multi-task learning should be evaluated on a case-bycase basis. Additional approaches have been used to provide additional context at training time, e.g., by learning associations between a small set of molecules of interest with a larger set of (contextual) molecules , . A particularly interesting strategy is meta-learning whereby the outputs of multiple machine learning algorithms are combined to predict a novel task, which is finding increasing application in drug discovery , , , e.g., to predict bioactivity on novel binding assays and protein targets .