Multi-modal semantics has relied on feature norms or raw image data for perceptual input. In this paper we examine grounding semantic representations in olfactory (smell) data, through the construction of a novel bag of chemical compounds model. We use standard evaluations for multi-modal semantics, including measuring conceptual similarity and cross-modal zero-shot learning. To our knowledge, this is the first work to evaluate semantic similarity on representations grounded in olfactory data.