MPNN-based molecular encodings promise a tunable representation that can suit any task through gradient descent. This allure has spurred much interest in applying graph models to various computational chemistry tasks. In this work, we show certain datasets are much more suited to MPNN-based models than others and that encoder architectures can have variable performance based on the character of the dataset used for training. In particular, we introduce the D4 Dopamine dock score prediction task, consisting of diverse ligands bound to one receptor.