We embedded ElMD distances in DensMAP space and clustered via HDBSCAN* to identify chemically similar clusters for 10 583 compositions. We introduced new proxies (i.e. metrics) for uniqueness-based materials discovery in the form of train contribution to validation log density, k-neighbor averages, and cluster validation fraction. By pairing these with the CrabNet regression model, we visualize Pareto plots of predicted bulk modulus vs. uniqueness proxy and obtain weighted uniqueness/performance rankings for each of the compounds. This reveals a new way to perform materials discovery with a focus towards identifying new high-performing, chemically distinct compositions.