Generally, however, CASSCF results are not quantitative and a range of post-CASSCF treatments of the remaining correlation outside the active space are employed to yield quantitatively accurate energetics. Two common options are the n-electron valence perturbation theory (NEVPT2) and the multiconfiguration pair density functional theory (MC-PDFT). The success of these multireference approaches is evident in several studies including the examination of catalytic cycles, photochemical processes, and magnetic properties. However, the central drawback of CASSCF-based approaches is that they are heavily reliant on the choice of the active space. Most often, the active space selection process is done manually and in trial and error by the practitioner. This approach prevents the highthroughput use of multiconfigurational methods, makes such methods unattractive to the wider computational chemistry community, and is broadly unsystematic. As a result, a host of systematic approaches to active space selection have been proposed. These include using natural orbital occupancies from unrestricted Hartree-Fock, gauging performance on dipole moments, and machine learning models. AVAS is a semiautomatic process in which the user selects a set of atomic orbitals and the molecular orbitals which sufficiently overlap are selected. AutoCAS presents a fully automated scheme where exact orbital entropies are calculated from a large active space (and partially converged) DMRG calculation and then active orbitals are selected by a simple threshold scheme or a more involved approach involving identifying plateaus in orbital entropies. Here, we make use of the approximate pair coefficient (APC) method, which provides a way to estimate orbital entropies from Hartree-Fock matrix elements priror to multireference calculations. To improve performance, this method is combined with discrete variational selection (DVS), which allows for the a priori selection between different active spaces proposed by APC on the basis of minimizing the average MC-PDFT energy across states of interest. We have previously highlighted the success of both methods for accurately automating active space selection for small molecule vertical excitations and organic reactivity. For vertical excitations in particular, the DVS-APC approach with hybrid MC-PDFT energies of DMRGCI wave functions was shown to yield a mean unsigned error of 0.17 eV relative to theoretical best estimates for roughly 200 small to medium main-group systems in the QUESTDB database. Transition metal containing systems present further complexity for active space methods and are thus a prime area for expanded testing of the DVS-APC approach. A recent addition to the QUEST database consists of 67 theoretical best estimates (TBEs) for vertical excitations spread across 11 first-row transition metal diatomics. Here, we test the performance of DVS-APC in selecting active spaces for this difficult set of vertical excitations, as well as for two larger transition metal complexes, MnO - 4 and [Cr(CN) 6 ] 3-. We find DVS-APC produces a NEVPT2 mean absolute error (MAE) of 0.18 eV, which is higher than the reported accuracy using manually selected active spaces (0.14 eV). In contrast, using minimizing error (ME) selection instead of DVS selection (i.e., selecting the APC active spaces in best agreement with the TBE values) results in an MAE of 0.10 eV. These differences in performance draw attention both to the limitations of the current DVS approach and the bias inherent in manual active space selection.