Recent years have seen an explosion of interest also in computational enzyme design, propelled in large part by early successes in de novo enzyme design through grafting computationally designed active site models onto natural protein scaffolds (e.g. refs. , among others). We note, however, that while impressive, this approach typically generates enzymes with only modest catalytic activities, which again require many rounds of directed evolution before reaching catalytic efficiencies that are comparable to naturally occurring enzymes. In light of the above, the use of computation to focus and speed up directed evolution is of considerable interest. Indeed, there have been substantial advances in this field, with many new screening approaches being put forward, based on sequence, structural or even dynamical information gained from simulations (see e.g. refs. ). In addition, machine learning shows great promise as a screening tool in enzyme design studies. Still, the best engineered enzymes, with catalytic efficiencies comparable to natural enzymes, are more often the results of intensive directed evolution efforts starting from low-activity rational designs. The sluggishness of the common directed evolution procedures has to do, at least in part, with the fact that most variants in a random library with a substantial mutational load will include mutations that are deleterious in terms of fundamental protein biophysical properties, such as stability and folding. FuncLib 28 is a novel automated method for designing multipoint mutations at enzyme active sites by combining phylogenetic analysis and Rosetta design calculations.