To demonstrate our methodology, we analyze a control dataset of materials with varying degrees of similarity and apply the approach to lithiumcontaining compounds from the GNOME dataset to assess potential synthesis targets. Through these analyses, we show that our method provides explainable density estimates that conform to researcher intuition while offering new insights into the ways materials might be novel relative to existing compounds. This approach is expected to aid both experimental and computational materials scientists in selecting synthesis targets according to relative novelty in addition to predicted performance metrics.