In this work, we introduce the Descending from Stochastic Clustering Variance Regression (DiS-CoVeR) algorithm, which unlike previous methods, screens candidates that have a high probability of success while enforcing -through the use of a novel loss function -that the candidates exist beyond typical materials landscapes and have high performance. In other words, DiSCoVeR acts as a multi-objective screening where the promise of a compound depends on both having desirable target properties and existing in sparsely populated regions of the cluster to which it's assigned. This approach then favors discovery of novel, highperforming chemical families.