Learned embeddings can also be used for a common cheminformatics task: similarity search and retrieval. Given some query molecule (Figure ), the goal is to find "chemically similar" molecules that are nevertheless different and diverse with respect to the initial query. This workflow may be used to design a library of chemical matter for high-throughput virtual screening or assays, or to preserve desirable characteristics of a query molecule while "hopping" to new molecular scaffolds or discarding undesirable moieties. The molecules identified as similar depend sensitively on the similarity/distance metric used, and the molecular representation. We propose a general representation learning problem defined as returning a distribution of nearest neighbors that comprise a smooth manifold in property space. That is, regardless of the representation and distance metric, the objective is to identify molecules that are similar to the query molecule in property space. This framing transforms the abstract problem of defining "chemical similarity" into the more tractable, easily understood problem of identifying molecules with similar properties. For simplicity, we consider molecular weight (MW), partition coefficient (LogP), quantitative estimate of druglikeness (QED), and synthetic accessbility (SA) , although any accessible properties could be used.