Originality: The proposed model (Molecule-Chef) is a novel combination of existing deep learning models - like Graph Neural Networks, RNNs, etc. to solve the task of molecule searching and to provide a synthesis recipe for the same. The authors also ensure the validity of products by restricting the latent space to chemical reactants that are readily available to chemists. Quality: The authors compare Molecule-Chef with state-of-the-art baselines and report improved/comparable results. By restricting the latent space, the model produces more valid molecular products as compared to other models. The paper also presents results of retrosynthesis, in which given some molecular products, the decoder of Molecule-Chef can be used to generate the possible combinations of reactants that were used to create the product.