Computational chemistry is about making models that simulate the behaviour of real chemical entities. These models can then be used as a predictive tool. For example, in high throughput screening of a library of computationally optimized molecules to find catalysts for drug design. In the screening there need to be values on which it is screened, one type of possible value can be descriptors, a numerical representation of the molecule. It is thus important that these molecules are as accurate as possible to get representative descriptors, but also relatively fast. As to make a library a lot of optimizations are performed, thus driving the costs up if the optimization takes a long time. To optimize a molecule its energy is needed. Calculating the energy of a system is done using quantum mechanics and is an integral part of computational chemistry. Many methods can be used to approximate the energy of a system. One such way of calculating the energy is DFT. DFT depends on a functional and a basis set, which the user must choose. This study is the descriptor-based benchmarking of three basis sets, def2-SV(P), def2-TZVPP, def2-QZVPP and five functionals, PBE, TPSS, PBE0, B3LYP and MN15. The optimisations’ results are compared using different methods using three molecular descriptors (bite angle, buried volume, and HOMO-LUMO gap). The third part of this study uses a combination of optimisation methods to try and improve the previous results. The different methods were compared by comparing the optimisation times and the descriptors to the standard of PBE0/def2-SV(P). The structures were optimised using Gaussian, and the descriptors were calculated using the in-house workflow OBeLiX. When comparing the basis sets, it was noted that def2-QZVPP took too much time to be of use and was thus not used in further comparisons. def2-TZVPP took substantially longer to complete than def2-SV(P). Looking at the descriptors, there was no difference between them. This led to the conclusion that def2-SV(P) was the optimum basis set for these 192 complexes as it was faster but had the same accuracy. When comparing the functionals, there was the surprising result that the choice of functional did not impact the chosen geometric and steric descriptors of bite angle and buried volume. The electronic descriptor, the HOMO-LUMO gap, differed greatly per method. The lower-level theory PBE and TPSS had a very low value compared to the hybrid functionals but were close to each other. MN15 had a HOMO-LUMO gap that was substantially higher than B3LYP and PBE0. The assumption was thus made that B3LYP and PBE0 were the most accurate functionals in this case. Looking at the time needed for the bulk of the optimisations to complete, PBE was by far the faster functional and MN15 the slowest, PBE0 was located in the middle of the pack. As PBE0 has a shorter optimisation time than B3LYP, the conclusion was that PBE0 was the optimal functional to use in this case. The third part of the study looked at combining optimisationmethods to see if a faster optimisation could be achievedwith the same accuracy. Here the base was a fastway, such as GFN2-xTB and PBE, to calculate the geometric and steric properties and then use a PBE0 calculation to make the electronic descriptor as accurate as when doing a general PBE0 optimisation. The best option was doing a single-point PBE0 calculation after a PBE optimisation. It was much faster than a PBE0/def2-SV(P) optimisation and as accurate. Making it an excellent way to optimise the TM complexes.