Even though all works operate on the same class of materials and often even use related machine learning approaches, these works are hardly comparable or replicable and are only implementable by experienced groups. This impediment is present across all stages of the machine learning workflow. Researchers use different datasets to train and test their models-as we show, sometimes with significant data leakage -preventing direct comparison of modeling approaches. Further down the modeling pipeline, practitioners often use different implementations of the same technique to convert structures into feature vectors-or do not attempt to try different strategies due to implementation challenges. At the end of the modeling process, models need to be validated. However, also there, researchers use different protocols, and-as we discuss-not always the most meaningful ones. Together with the lack of platforms that compile the results obtained with different approaches, these bottlenecks make machine learning for reticular chemistry still more an art than a science.