While Cage-VAE is the first generative model specialised in cage molecules, it also has limitations. The performance of the cage generation is robust, however, the predictions of shape-persistence may differ from the calculations obtained by MD simulations in certain cases. This can be traced back to the deviations in predictions in the shape persistence predictor, where the trained mappings from latent representations of cages to the property are not generalised to the synthetic cage samples far away from the current distributions due to the lack of labelling. Comparing the two strategies, molecular optimisation is observed to have more frequent erroneous predictions than the interpolation methods, as the molecules sampled using BO are more likely to have novel structural features and be away from the original distributions. In addition, the predictor included in our model is a general model designed for all reactions. Turcani et al. revealed discrepancies in predictive performances among cages assembled by different reactions. This is likely to be a source of error introduced to the general predictor as it needs to capture different patterns in reaction types and balance different features to obtain the overall best predictions. Finally, the presented VAE model is trained with minimal chemical knowledge and the shape persistence information is provided by the external predictor. In future work, the combination of more chemical knowledge explicitly or implicitly can be combined to fine tune the generative model and reorganise the latent space. In addition, the cage encoding could incorporate the emerging SELFIES representation to explore the capability of conditional POC generation.