Table shows that the QED value was more than 0.92 for the top few cases, which is approximately sufficient for optimization. Furthermore, MERMAID had a lower evaluation function value of 0.3 to 1.4. This is also visualized in Fig. . The difference between the two methods is the representation of molecules: MERMAID uses SMILES, and the proposed method uses molecular graphs. Considering the ZINC dataset as an example of "data size," the average number of nodes in a molecular graph was approximately 24, while the average number of tokens in SMILES was approximately 40. Since both methods search only one node/token per step, this method was considered to search more when compared with the same number of steps. Additionally, while SMILES generated invalid SMILES, the molecular graph was always valid, which is another reason this method was able to search more. Note that SMILES with RNNs generally runs faster than molecular graphs with graph convolutional networks (GCN) when compared in a single step.