A major challenge in the application of block copolymer directed self-assembly (DSA) to advanced lithography is the exploration of large design spaces, including the selection of confinement shape and size, surface chemistry to affect wetting conditions, copolymer chain length and block fraction. To sweep such large spaces, a computational model is ideally both fast and accurate. In this study, we investigate various incarnations of the density functional theory (DFT) approach and evaluate their suitability to DSA applications. We introduce a new optimization scheme to capitalize on the speed advantages of DFT, while minimizing loss of accuracy relative to the benchmark of self-consistent field theory (SCFT). Although current DFT models afford a 100-fold reduction in computational complexity over SCFT, even the best optimized models fail to match SCFT density profiles and make extremely poor predictions of commensurability windows and defect energetics. These limitations suggest that SCFT will remain the gold standard for DSA simulations in the near future.