in the latent space. For example, in text-based models, wordembeddings represent the latent features for each word in the context of the sentence after training. In chemistry, atomembeddings, also optimized through training, are the features representing an atom in the context of a molecular graph. These neural activations for the word/atom are tuned to hold meaningful information about the context of the data (i.e. words, molecules) and the specific input. However, they are generally high-dimensional and obscure objects to analyze on their own. In previous works, we showed that the embeddings of chemical GNN models hold valuable information about chemistry such as the ability to distinguish molecular environments and the ability to quantify molecular similarity. We also showed that the chemical embedding space is a readily transferable representation for a wide array of properties such as for pKa, NMR, and solubility, underscoring the completeness of these representations. In this work, we go beyond the locality of the representation, and show that graph-embeddings behave similar to text-embeddings in that they have arithmetic properties that reveal meaningful combinations, akin to how word-embeddings can be combined to make word analogies. This will naturally uncover the chemical syntactical organization of the embedding space.