Enzymes are green catalysts with unmatched catalytic proficiencies, and with widespread applications in biotechnology as extracellular catalysts for a host of (bio)chemical processes, from organic synthesis to developing new pharmaceuticals, biofuels, or bioremediation agents, to name but a few examples (see e.g. ref. for an overview). To be able to efficiently control the physicochemical properties of enzymes in a tailored fashion is therefore a problem with major economic implications, leading to extensive research effort in this direction. However, natural enzymes have had millions of years to evolve to their modern catalytic efficiencies, and therefore mimicking this process whether in vitro or in silico is a non-trivial undertaking, in particular due to the immensity of the sequence space that needs exploring, and the very high frequency of catalytically detrimental mutations. Directed evolution revolutionized experimental protein engineering efforts, by vastly expanding the sequence space accessible to protein engineers by several orders of magnitude, with low overhead. Despite its many advantages, as a caveat, directed evolution is time-consuming, typically requiring many rounds of medium or highthroughput screening to achieve suitable levels of enzyme catalysis from a starting, low seed level. Nevertheless, it has facilitated the development of a wide diversity of biotechnological applications of proteins.