To break down the 24,000 predicted molecules into a more manageable number, we then applied the previously described filtration method to find 20 representative structures to relax per model. Namely, all generated structures are k-means clustered as described in the methodology and only the structure closest to each cluster center is relaxed. After relaxation using DFT we used RDKit to parse the molecules to SMILES and discarded all stereoisomer information, these SMILES were then used to compare our results with the ground truth. Fig. (left) depicts the structures each model was able to identify, where again we find that none of the models alone was able to find all possible C 5 H 12 O isomers, but the union of the relaxed structures contained all target molecules. Fig. (left) furthermore shows that both different hyperparameters and different model initializations with the same hyperparameters can help the model to collapse into different basins. For example, the first model with R cut = 5 and n max = 3 fails to recover five structures (white boxes in the leftmost column), which are all found by the second model with R cut = 5 and n max = 3 except for a single structure. Likewise, the structure most often discovered in the first model is not discovered even once by the second model.