For this reason, we extended our system to automatically search for structures that optimally match the available experimental evidence. In practice, chemists might input a measured spectrum (and optionally a first guess), and then our pipeline searches for the compounds that best match the provided spectra. To do so, we utilize modified genetic algorithms that directly operate on the structure graph of the chemical structure and evolve the structure (by adding, removing, or changing atoms, bonds, or entire substructures) to better match the experimental evidence. We again use the cosine similarities between the aligned embedding vectors to measure the match between the structures and the spectra.