The advent of machine learning (ML) and related big data approaches has made it possible to more efficiently search through MOF chemical space, and high-throughput computational screening can often provide insight into previously unknown structure-function relationships. With this goal in mind, a high-throughput density functional theory (DFT) workflow was recently used to construct a publicly accessible dataset of quantum-chemical properties for thousands of MOFs (and coordination polymers), known as the Quantum MOF (QMOF) Database. Like many databases of material properties generated from high-throughput periodic DFT calculations, the electronic structure properties within the QMOF Database were computed with the relatively inexpensive Perdew-Burke-Ernzerhof (PBE) exchangecorrelation functional. While PBE is useful for generating large quantities of material property data that are often needed for ML, the electron self-interaction error of generalized gradient approximation (GGA) functionals like PBE can greatly influence the predicted electronic properties. Perhaps most notably, PBE is known to severely underpredict band gaps, but the degree to which there may be qualitative (as opposed to merely quantitative) errors is not well-established. This inherently limits the practical utility of data-driven, computational screening approaches based on such a functional.