Because full structure optimisations in the framework of the DLPNO-CCSD(T) method are not feasible at present, only single-point calculations were carried out. To evaluate all methods on an equal footing, the DFT adsorption energies were computed for the same cluster models, cut out from periodic structures in which the position of acetaminophen was optimised using the PBE-D3 functional (dubbed "frozen" clusters in the following because no further optimisations of the clusters were performed). In order to test whether the trends in adsorption energies across the set of functionals that were established in Part 1 are affected by the use of such frozen cluster models, rather than periodic models optimised with the dc-XC functional of interest, the adsorption energies were recomputed for the cluster models. These calculations compared TZVP-MOLOPT basis sets to triple-and quadruple-zeta GTH basis sets, the latter of which also allowed for a CBS extrapolation. The results are summarised in Table .1. The adsorption energies obtained for clusters are significantly less negative, which is not surprising given the missing contribution of long-range dispersion interactions. When calculating a linear regression between adsorption energies obtained from calculations for frozen clusters and those obtained from periodic calculations, the squared correlation coefficients are ≥ 0.90 for individual GTH or MOLOPT basis sets, and fall between 0.86 and 0.91 when using CBS extrapolation for the cluster models. It is worth noting that the correlation coefficients increase to values of ≥ 0.93 if only a single functional, namely the vdW-DF functional, is omitted, and the correlation improves even further if vdW-DF-cx is also omitted (with all 𝑅 2 values being ≥ 0.96). At this point, it remains unclear why a larger difference between periodic and cluster calculations occurs for these two functionals. By and large, however, the analysis convincingly shows that the trends among the different functionals observed previously for periodic structure optimisations are retained, validating the outlined strategy of using single-point calculations on clusters cut out from the PBE-D3 optimised structures in the benchmarking of DFT approaches against DLPNO-CCSD(T) calculations. Since the reference DLPNO-CCSD(T) calculations were performed with ORCA, using the Ahlrichs (denoted "def2") basis sets, it was evaluated in the next step whether adsorption energies obtained with such calculations are directly comparable to those of CP2K calculations with GTH basis. Since only some of the dc-XC functionals listed in Table are implemented in ORCA, five (meta-)GGA+D3 functionals were included in this comparison: PBE-D3, revPBE-D3, BLYP-D3, BP86-D3, and TPSS-D3. The results are compiled in Table .2, which also includes the mean of absolute error 𝑀𝐴𝐸 of CP2K versus ORCA adsorption energies for comparable basis set sizes. When employing TZV2P-GTH and QZV3P-GTH basis sets in CP2K, the 𝑀𝐴𝐸 values obtained for different functionals are on the order of 1.2 to 2.3 kJ mol -1 and 2.1 to 3.5 kJ mol -1 , respectively (reference: ORCA def2-TZVPP and def2-QZVPP adsorption energies). Interestingly, CP2K results obtained with TZVP-MOLOPT basis sets fall more or less in the middle between the ORCA def2-TZVPP and def2-QZVPP adsorption energies. The most impressive agreement, however, is achieved by extrapolating to the CBS limit: CBS extrapolation of CP2K TZV2P-/ QZV3P-GTH energies delivers 𝑀𝐴𝐸 values that do not exceed 1.1 kJ mol -1 when CBS-extrapolated ORCA def2-TZVPP/def2-QZVPP results are taken as reference. It can thus be inferred that CBS extrapolation of TZV2P-/QZV3P-GTH energies in CP2K delivers 𝐸 𝑎𝑑𝑠 values that can be directly benchmarked against (also CBSextrapolated) DLPNO-CCSD(T) values calculated with ORCA, because systematic differences stemming from the choice of basis sets and code are significantly smaller than typical differences between DFT and DLPNO-CCSD(T) adsorption energies.