Across most areas of chemistry, Kohn-Sham Density Functional Theory 1,2 (KS-DFT, often shortened to just DFT) has become a common part of understanding reactions and structures, either alongside experiments or on its own. One particular application is the study of enzymes, where computational results can be used to understand the mechanisms of action and gain structural insights, which can then be applied to enzyme design or general catalysis applications. Generally these studies are done by treating a curated model of the active site with DFT and calculating the indirect effects of the remaining enzyme ei-ther as an electrostatic field, as in cluster model studies, or with forcefields, as part of a hybrid quantum mechanics/molecular mechanics (QM/MM) scheme; for reviews of QM/MM approaches for enzyme modeling, see refs. 3 and 4. While DFT is often a good choice for computational studies due to its accessibility and cost-to-accuracy ratio, there are an overwhelming number of density functional approximations (DFAs) available to users, each having been determined from different sets of data or first-principles boundary conditions, and therefore having different strengths. The choice of DFA can thus alter the conclusions drawn about possible mechanisms or structural features, so it is important to consider the choice carefully. This process is assisted by benchmarking studies that test the performance of density functionals, either on comprehensive databases of chemical reactions in order to find generally robust functionals, or test sets that are more specific to the chemical problem at hand. Some examples of broad benchmark sets that represent general main group chemistry are GMTKN55 5 and MGCDB84, across which hundreds of DFAs have been tested-more than 350 by our group alone. There are also smaller test sets that focus on organic biochemical reactions, including work by us, as well as sets that represent functional groups and reaction types often found in, but not exclusive to, enzyme chemistry. However biochemistry is not limited to main group elements. It is estimated that around one third of the human proteome requires metals to function, and metalloenzymes are crucial in many other living species too. Unfortunately transition metals are harder to treat accurately with computational methods as their electronic structure is more complex, so recommendations of DFAs from tests on organic enzymes are not necessarily applicable to metalloenzymes. Due to difficulties in getting reliable reference values, there are significantly fewer benchmark studies that represent transitional metal chemistry. Most existing test sets feature inorganic dimers or small organometallic complexes, or only include a small number of systems, and these are all typically closed shell species. Various groups are working on filling the gaps of larger complexes and open shell species, particularly with the WCCR10, MOR41, 50 MOBH35 51 and ROST61 sets, and recent studies assessing Coupled Cluster for calculating reference values for these types of systems. Chan et al. have also compiled the TMC151 database from sets of organometallic reaction energies, organometallic barrier heights, and inorganic dimer bond energies, to be used as a (smaller) counterpart to the existing broad main group databases and find functionals that are robust across multiple types of transition metal chemistry. Within computational bioinorganic chemistry, DFT has been tested for a range of properties, including spin state splitting, metal-ligand binding and interaction energies, excited states, relative energies of Cu 2 O 2 isomers, redox potentials, 66 and bond dissociation energies. Many studies of the mechanisms or properties of a specific enzyme/family of enzymes also validate the chosen DFT methodology with a comparison to ab initio methods or other density functionals. Most of these data, however, are small scale, both in the range of systems included and the methods tested, and were not tested against reliable references. We also note that some studies use small simplified models, an approach which does not reliably represent the range of interactions found in an enzyme. In our recent guide to benchmarking enzymatically catalyzed reactions, we have shown that oversimplified models can have very different benchmarking outcomes compared to larger models for organic enzymes, as well as how the quality of the references will change the perceived performance of the tested functionals-a fact that has also been shown before for pericyclic and inorganic reactions. We have, thus, sought to create a benchmark set for metalloenzyme reactions to sit between our previous set of mostly organic enzyme models and the MOBH35, MOR41 and ROST61 sets for transition metals, to explore whether the recommendations from these previous studies hold for bioinorganic systems. We additionally hope that by adding more data in the overlap of biochemical and organometallic benchmarking, this set can assist bioinorganic chemists in choosing reliable functionals for their calculations, or method developers in creating new functionals for accurate transition metal calculations.