where X v,j , µ t,i , Σ t,i , •, and n train represent j-th validation DensMAP embedding position at which to be evaluated, i-th train DensMAP embedding position, i-th train covariance matrix, tensor product, and total number of train points, respectively. By doing so, we obtain a proxy for chemical uniqueness relative to existing materials. By combining high-fidelity CrabNet predictions of bulk modulus with DensMAP validation densities, we extract a list of promising compounds at the Pareto front -the line or "front" at which the trade-off between performance and chemical uniqueness is optimal. CrabNet predictions have been shown to be comparable to state-of-the-art composition-based materials regression schemes, and since structure is often not known during a materials discovery search, CrabNet is a reasonable model choice. One partial workaround for the limitation of structure being unknown a-priori has been explored in the Bayesian Optimization With Symmetry Relaxation algorithm , which may be of interest to incorporate into DiSCoVeR in future work. Additionally, by performing leave-one-clusterout cross-validation (LOCO-CV) , we accurately sort the list of validation clusters by their average performance with a scaled sorting error of approximately 1 %. This proof-of-concept strongly suggests that DiSCoVeR will successfully identify the most promising compounds when supplied with a set of realistic chemical formulae that partly contains out-of-class formulae produced via a suggestion algorithm. To our knowledge, this is a novel approach that has never been used to encourage new materials discovery as opposed to incremental discoveries within known families.