Science is advancing at an increasingly quick pace, as evidenced, for instance, by the exponential growth in the number of published research articles per year. On the one hand, this poses a pressing challenge: Effectively navigating this ever-growing body of knowledge is tedious and time-consuming in the best of cases, and more often than not becomes infeasible for individual sci[1]entists. On the other hand, from an AI point of view, scientific literature offers a great opportunity: The body of published research works offers a vast collection of highest-quality—literally expert-reviewed— data about the relationships of concepts and the governing laws of our physical world. Making use of the opportunity in order to mitigate the challenge, computational systems have been introduced which aim to support human researchers in the initial phase of the scientific process by automatically extracting hypotheses from the knowledge contained in published resources, i.e., by performing automated hypothesis-generation. In this talk, I will summarize our team’s recent efforts in developing and validating a fit-for-purpose hypothesis-generation system for the biomedical sciences.