Heterogeneous catalysis is an essential technology for enabling sustainable economic development. On one hand, chemical processes like ammonia-synthesis require massive amounts of energy and are thus substantial greenhouse gas emitters. On the other hand, the long term storage of renewable energy in synthetic fuels is itself a catalytic process. In both cases, new and improved catalysts would therefore yield large benefits towards reducing global net carbon emissions. While new catalysts have historically often been found by serendipity or empirical insight, theoretical understanding has played an increasingly significant role over the last decades. Indeed, not least the fundamental theoretical understanding of catalyst functionality based on scaling relations (limited as it may practically be) has led to the emergence of an entire field of computational screening based catalyst discovery. Such a computational catalyst screening requires first to define a library of candidates (i.e. a chemical space, see Fig. ). This space is typically constructed according to some simple rules (e.g. the set of ordered metals or solid solution alloys in a fixed lattice) or taken from some predefined experimental or computational database (e.g. the Materials Project 6 ). Once this space is defined, the screening itself consists of computationally estimating the catalytic activity of all candidates (or representative samples) contained therein.