Mining of novel enzymes. Nature provided us with an incredible repertoire of catalytically efficient enzymes. Thanks to advances in sequencing technologies, we can access vast repositories of protein sequences, potentially coding for the biocatalysts of interest. Automated systems like EFI-EST and EnzymeMiner 31 are based on searches of homologous sequences, multiple sequence alignments and extensive annotations. Searches can be complemented by an assessment of essential properties, like solubility or aggregation . A critical comparison of catalytic properties of mined and engineered haloalkane dehalogenases over three decades revealed that mining was a more successful strategy than engineering for this class of enzymes . The constant growth of genomic and metagenomic databases will require new technologies to correctly identify hits within a reasonable time frame, motivating quantum search algorithms. Moreover, with the advent of accurate protein structure prediction using AlphaFold , we wish to start using structural bioinformatics for all-againstall docking and calculation of chemical reactions using quantum mechanics. Quantum simulations will provide a framework for an efficient search of protein/ligand databases and combinatorial execution of quantum calculations 36 with multiple protein structures and multiple ligands, which is impossible using current classical computers. Systematic analysis of multiple enzyme-substrate complexes will provide information on catalytic efficiency as well as substrate specificity.