In this study, a method which intimately integrates both aspects, "Which" and "How", namely, the de novo generation and the chemical synthesis of a given compound, is suggested. The proposed method can be used to rapidly expand the chemistry space in an organization through taking advantage of the internally available compounds and building blocks as well as the internal reaction data. However, to make this study reproducible we have used publicly available data. The method is based on a knowledge graph, encoding reactions from USPTO (United States Patent and Trademark Office) , and is outlined in Figure . In the first step, SEAL (learning from Subgraphs, Embeddings and Attributes for Link prediction), a graph neural network-based link prediction method, is applied on the chemical reaction graph to infer novel possible reactions by predicting the reactivity between pairs of reactant molecules . In the second step, pairs of reactants presenting a predicted reactivity above a given threshold are channeled to Chemformer, a molecular transformer model, which is used to generate the potential reaction products . Thus, the de novo compounds are generated simultaneously to the identification of the required reactants which are selected for high probability to lead to a productive synthesis. In this way, our method can be used to expand the current chemical space by including the predicted synthesizable reactions. Thus, we can efficiently create a new virtual chemical space containing novel synthesizable chemistry. In Section 2, the details of the Chemical Reaction Knowledge Graph (CRKG) used in this work is introduced. The applied link prediction algorithm as well as the molecular transformer model used to predict the reaction products is also explained. Thereafter, in Section 3, we show the results from the respective models on our data. Finally, in Section 4, the use of the developed method for a hit finding campaign targeting the discovery of novel dopamine receptor D2 (DRD2) modulators is showcased.