Density functional theory (DFT) has proved to be exceptionally successful in rationalizing trends in activity and functionality for electrochemical functional materials. With continued increases in computing power, there has been an increased interest in “high-throughput” materials discovery and design based on a few descriptors to scan the phase space en masse for thousands of potential candidates, which could be made technologically and commercially viable. However, given fundamental accuracy limitations associated with DFT, the success of high-throughput material discovery efforts has been limited. In this review, we suggest an additional dimension to aid in high-throughput material discovery related to uncertainty quantification and propagation, which provides a more realistic picture of the likelihood of new candidate materials to improve upon known materials. We demonstrate the approach and its utility through two case studies: (1) electrocatalyst materials for their activity and selectivity for the oxygen reduction reaction, and (2) cathode materials for Li-ion batteries based on Ni-Mn-Co oxides. The ease with which uncertainty quantification and propagation can be incorporated into traditional high-throughput material discovery with almost no additional computational cost allows for its proposed wide usage.