Discovering novel and more effective therapeutics is an important research problem in which deep learning is playing an increasingly pivotal role. However, real-world drug discovery tasks are often characterized by a scarcity of labelled data and significant data shifts. My research focuses on developing more robust and data-efficient deep learning methods that perform well in this setting, with a particular focus on improving out-of-distribution generalisation and generative modelling under domain-informed constraints.