for well-chosen active spaces are generally obtainable with practical values of M ). Using this approach, it is possible to describe active spaces with up to about 100 orbitals. The success of CAS-based methods relies heavily on the selection of the orbitals for the active space; this selection affects both the convergence of self-consistent-field iterations and the quality of the energetic results. Variationally optimizing the active-space orbitals is known as CAS self-consistent field (CASSCF) when used with a full-configuration-interaction solver or as DMRGSCF when used with a density-matrix-renormalization-group solver. To try to obtain a consistent treatment of multiple states, one may optimize the state-averaged (SA) energy with respect to the active orbitals, yielding SA-CASSCF and SA-DMRGSCF. However, we emphasize two issues with this approach: (i) the energetic optimization is prone to converging to local minima, and (ii) variational optimization of state-averaged CASCI energies may not be optimal for computing energy differences between states. Furthermore, orbital optimization significantly increases the cost of the computation. Thus, although SCF generally helps improve the quality of the active space, it does not eliminate the need to develop good active space selection schemes and comes at a computational cost.