The ChEMBL database is one of the most significant resources for measured small-molecule bioactivity data. It currently holds 2.4 Million compounds with more than 20 Million bioactivities recorded for a total of more than 15,000 targets. While this documents substantial scientific advances, these numbers are dwarfed by the size of the drug-like chemical space, which is estimated to comprise 10 23 to 10 60 compounds (for an overview, see Polishchuk et al., 2013), and the human proteome, which is composed of around 20,000 proteins. Aggravating data scarcity is the reciprocal relationship between novelty and data availability: for the most interesting biomolecular targets and the most innovative chemical space, data is, by definition, particularly scarce. Training machine learning models directly on small amounts of data can lead to overfitting, meaning the resulting models will likely perform poorly under real-world scenarios (i.e., unseen, new tasks). Numerous knowledge-sharing methods have been devised and are increasingly used to address the challenges stemming from the persistent data shortage. These include transfer learning, multi-task learning, and meta-learning. Common to all knowledge-sharing methods is that they use information from related tasks ("source tasks" or "training tasks") or datasets to improve the performance on tasks for which typically low and scarce amounts of data are available ("target tasks" or "test tasks").