DLPNO-CCSD(T)/TightPNO is regularly used to calculate reference values in benchmark studies of both organic and organometallic systems, including our work on models of enzymatically catalyzed reactions. Recent work has shown that the efficiency and accuracy of TightPNO calculations can be improved by altering the T CutP N O parameter, which determines the number of PNOs used in the correlation treatment for each electron pair-PNOs with an occupation number below the threshold value are discarded. The default value of T CutP N O for TightPNO is 10 -7 , but this can be approximated with extrapolation of values calculated with T CutP N O = 10 -5 and 10 -6 (using the TightPNO default values for all other thresholds), at a significantly reduced computational cost. On the other hand, if TightPNO calculations are feasible, then the accuracy can be improved through extrapolation of T CutP N O = 10 -6 and 10 -7 results, approximating values calculated with the tighter value of T CutP N O = 10 -8 . These extrapolated results have been labeled complete PNO space (CPS), similar to complete basis set (CBS) extrapolated results, as they ideally approximate the energy obtained when no PNOs are pruned (T CutP N O = 0). The initial analysis of the CPS approaches on a range of GMTKN55 15 subsets showed that CPS (5,6) performed similarly to TightPNO against canonical CCSD(T) references (mean absolute errors (MAEs) of <0.8 kcal/mol and <0.6 kcal/mol, respectively), while CPS(6,7) was almost converged with respect to the PNO truncation error, with its residual MAE of <0.27 kcal/mol likely coming from the other sources of error in the DLPNO procedure, namely the resolution of the identity (RI) approximation and selection of strong pairs. 22 CPS(6,7) results have been shown to improve upon standard TightPNO for organometallic barrier heights and spin state splittings. The PNO truncation error also strongly influences the size dependence of the DLPNO error, so its reduction makes a significant difference for larger systems. The accuracy of DLPNO-CCSD(T) compared to canonical CCSD(T) also improves when the newer iterative (T 1 ) triples procedure is used instead of the original quasi-perturbative (T 0 ) procedure, with DLPNO-CCSD(T 1 ) having been shown to perform better than DLPNO-CCSD(T 0 ) on a range of different chemical systems. The impact of the quality of the reference values has been shown for various benchmark sets, with different references often leading to different recommendations as the magnitudes of the deviations and relative ranking of functionals change. It is therefore important to carefully consider the level of theory used to calculate the benchmarks. Our aim in this work is to assess the CPS extrapolation scheme for the calculation of reference REs and BHs associated with enzyme active site models. Previous analysis has already focused on the accuracy of CPS results in comparison to canonical CCSD(T) or other ab initio methods, 24,26 so here we look at how much the outcomes of benchmark studies change when CPS-extrapolated reference values are used instead of standard TightPNO ones. For this we use the small test set described in figure , which contains ten reaction energies (REs) and 4 barrier heights (BHs) across active site models of seven enzymes-these are cysteine dioxygenase (CDO), hemocyanin (Hc) and superoxide dismutase (NiSOD) from our MME55 set of metalloenzyme models, and 4-oxalocrotonate tautomerase (4-OT), histone-lysine methyltransferase (HKMT), L-aspartate α-decarboxylase (AspDC) and haloalcohol dehydrogenase (HheC) from our set of mainly-organic enzymatically catalysed reactions. All of these are closed shell species. Our initial work on the latter set did not assign it a name, so we adopt a modified version of the label used by Förster and Visscher in their superset for testing double hybrid DFT and call the entire set ENZYMES28. EN-ZYMES28 additionally contained a model of the zinc-dependent enzyme phosphotriesterase (PTE), so we refer to the subset containing only the models of the four organic enzymes as ENZYMES22.We point out that all these models contain only parts of the active site, not the entire enzyme; our aim in these previous benchmark studies has been to recommend reliable DFAs for the active site region that can then be used for cluster model studies or combined with forcefield methods in layered QM/MM approaches. Geometries for all models have previously been optimised at the PBEh-3c level of theory, which contains the DFT-D3(BJ) correction to improve the description of London dispersion interactions, and we direct the reader to the Supporting Information of Refs. 19 and 21 for the provided structures in xyz format.