CGCNN-HD and MEGNet demonstrated a high success ratio, but low discoverability, which could be linked to the behavior observed in Figure , where the curves lean to the upper left. This is because ML models using distance-based features tend to overestimate Ef and Ehull (Fig. ) resulting in a small number of ML-predicted stable materials, making success ratio high and discoverability low. In this case, a large portion of stable materials could not be discovered. Conversely, Wren and Roost are positioned at the lower right, demonstrating low success ratio and high discoverability. This is because they tend to underestimate Ef and Ehull (Fig. and). Thus, many ML-predicted stable materials resulted in a low success ratio and high discoverability. Contrary to these two cases, ESNet achieved a balance between success ratio and discoverability, maximizing F1-score (Fig. ). This could also be evidenced by the P-R curve, where points of ESNet are positioned toward upper right compared to any other ML models (Fig. ). In this analysis, we confirmed that ESNet is most suitable for IS2RE, thus, in the following, we performed HTVS to find stable materials using ESNet.