Firstly, when large libraries of materials are tested, the detailed characterization of each material is typically not feasible. Thus, only little information on the structure and physicochemical properties of each compound might be available. This hinders the in-depth understanding of the underlying processes governing reactivity, which could be used for rational catalyst design. Secondly, distinct catalytic mechanisms might operate depending on the materials and reaction conditions, and only very few situations result in good catalytic performance. This leads to an unbalanced distribution between high-and lowperformance scenarios and brings into question the usefulness of global models to help deciding on the next materials to be tested. These models are trained to describe all materials and reaction conditions simultaneously by minimizing the expected average prediction error over all samples. While this approach may provide an accurate prediction on average, it does not necessarily allow for a focused modelling of the most interesting materials and mechanisms. Alternative approaches for catalyst design are therefore required.