Because of the above considerations, active-space selection remains a vigorous area of research, and several approaches have been proposed to date. The historically popular method involves chemical intuition with trial and error. However, this trial-and-error approach is unsystematic and difficult to apply in a high-throughput fashion. In recent years, there has been much interest in developing more systematic methods for active space selection. A tool called AVAS, developed by Sayfutyarova and coworkers, allows one to semiautomatize the active-space selection by constructing molecular orbitals that overlap optimally with a user-selected set of atomic orbitals. Other approaches involve some preliminary calculations, such as the natural orbital occupancies of a unrestricted Hartree-Fock (UHF) calculation, entanglement information from a large DMRG calculation, 41,50,76,77 the quantitative accuracy of some physical observable such as the dipole moment, machine learning predictive models, and physically motivated equations based on information such as HF matrix elements. An assumption of all these approaches is that the key physics necessary to select the active space can be captured by the preliminary calculation (UHF, DMRG, etc.). However, as we will show, even when using large active spaces (e.g., with 40 orbitals) for small molecules, it is difficult to determine active spaces that are adequate for any given excitation with a single method.