In principle, the full configuration interaction (FCI) method 1 solves the electronic problem exactly for a given basis, but it scales exponentially, rendering FCI impractical for all but the smallest of problems. However, it is well-known that the solution to the FCI wave function is generally sparse. That is, many determinants do not significantly contribute to the overall wave function; these determinants are sometimes referred to as computational "deadwood." 2 From a practical standpoint, this means that significant compression of the wave function is possible while retaining near-FCI accuracy. Approaches for wave function compression based on the singular value decomposition and compressive sensing, for example, have been explored. How to best exploit this sparsity is an open question, but it is a question with important ramifications.