When tested on the General test set, the models exhibit almost the opposite trend (Figure ). Performance of the fine-tuned model drastically decreases compared to the baseline model, with the top-1 accuracy dropping from 14.5% to 2.7% and top-1 round-trip accuracy from 87.4% to 52.6%. Meanwhile, the metrics for the mixed fine-tuned and multi-task model only change marginally, dropping by at most 2%. Ensemble decoding falls in between, with a top-1 accuracy of 9.3% and round-trip accuracy of 77.9%. The drop in performance observed with the fine-tuned model can most likely be attributed to catastrophic forgetting, the tendency of NNs to forget previously learned information when trained on new data. This drop can be disregarded if the model is only intended for one-step ring disconnection. However, it becomes problematic for multi-step retrosynthesis as the fine-tuned model will not be able to disconnect the linear intermediates obtained after disconnecting the ring. In that case, either the mixed fine-tuned or multi-task model would be more suitable.