In addition, while the FuncLib algorithm focuses on optimizing stability and carries no information about the transition states involved, nevertheless, the best performing FuncLib variants do so due to improved geometric preorganization of the active site through optimizing of the D-H distance and D-H…A angle. This suggests that, in particular for more complex systems where mutations can introduce larger changes in activity, the FuncLib-based stability-guidance could be further refined and focused on the basis of the computational prediction of catalysis, at least in the initial stages of the directed evolution process, during which larger jumps in activity may be possible. This is significant, as FuncLib does not take any information about the substrate or transition state into account in the design process, and therefore while it targets the stability of the overall protein, it does not provide insight into how mutations will affect transition state stabilization. Taken together, the combination of experimental and computational work presented here both showcases the tremendous potential of FuncLib's evolutionary-based stability-screening protocol as a valuable tool in computational enzyme design, as well as the potential of ancestral enzymes as starting scaffolds for artificial enzyme engineering. Here, our crystal structures illustrate that significant gains in activity can be achieved without the need for corresponding significant active site arrangement. Finally, it is important to note that FuncLib is based on sequence alignment, and thus it would be logical to assume that it would work best for enhancing the reactivity of an enzyme towards its native substrate(s). There remains, however, the question of whether it would also enable the design of function scaffolds that were not designed for those functions. By targeting a non-natural reaction in a de novo active site, we demonstrate that FuncLib is a broadly useful tool, that can also be used to design biological catalysts for anthropogenic substrates.