Engineering of highly enantioselective enzymes. The computational design of enantioselective biocatalysts is challenging, and the development of enantioselective enzymes often relies on focused directed evolution. Why is it difficult? Enantioselectivity is driven by the ratio of binding or Rand S-enantiomer and the ratio of their respective catalytic rates. Accessing enantioselectivity requires accurate modelling of two enzyme-substrate complexes and precise calculation of binding energies. Moreover, two reaction coordinates and transition states must be evaluated using high-level quantum mechanics. Accurate calculation of enantioselectivities using high-level quantum mechanics is possible , but it requires high skills and nontrivial computing power, preventing combinatorial searches of the effects of mutations on enantioselectivity. We can explain experimentally observed enantioselectivities, but we can not design them rationally. Quantum parallelism will allow simultaneous exploration of multiple reaction coordinates of mutants starting from enzymesubstrate configurations obtained by molecular docking or diffusion models . Moreover, calculations of electron dynamics using quantum simulations can provide higher accuracy . In our opinion, reproducible in silico design of enantioselective biocatalysts would be a clear demonstration of quantum advantage.