Imagine drawing the chemical reaction of interest, converting it into chemical structures, and screening millions of predicted protein structures for potential binders . Next, we will conduct highly parallel quantum-mechanical calculations of the reaction coordinates for thousands of selected enzyme-substrate complexes to identify enzymes with low activity . Machine learning models will filter hits based on protein stability, low aggregation, and high expression in the preferred host organism . Massively parallel molecular dynamics will explore significant conformational changes during substrate binding and product release, identifying bottlenecks and potential substrate and product inhibitions. By integrating data from quantum mechanics, molecular dynamics, and machine learning, we will be able to (i) predict enzyme-ligand complexes with atomic accuracy, (ii) quantitatively assess protein fitness, (iii) identify rate-limiting steps, and (iv) select "hotspot" regions for in silico mutagenesis. Combinatorial substitutions and indels will be introduced to these hotspot regions, followed by iterative cycles of calculations to gradually fix beneficial mutations and improve protein fitness. While we have the majority of algorithms and models available, we lack the computing power for in silico protein evolution. Computationally evolving highly efficient, enantioselective, and expressible biocatalysts would be a clear demonstration of quantum supremacy in the biocatalysis domain.