In the past 20 years, quantum chemistry has made great strides in describing chemical reactivity; widely-used methods such as density functional theory (DFT) and coupled-cluster methods (e.g., CCSD(T)) have become a rich source of data for the understanding of chemical reactions and the development of machine learning algorithms. However, despite their black-box nature, these methods face limitations on systems poorly described by a single electronic configuration, i.e. multiconfigurational or strongly correlated systems. A key example of these systems is familiar to most chemists: that of the transition state, in which the electronic character is often split between describing that of the reactant and the product. Given the ubiquitous nature of transition states in chemistry, it may then be a wonder how these approaches have proven successful in so many applications. The answer is that for many important cases these methods are simply able to overcome this difficulty despite the fundamental struggle with multiconfigurational character. Nevertheless, in automated applications of quantum chemistry such as reaction network exploration, the poorer description of multiconfigurational species can rear its head in key places and significantly impact results.