Recent advancements in self-supervised learning and transfer learning methods have popularized approaches that involve pretraining models from massive data sources and subsequent finetuning of such models towards a specific task. While such approaches have become the norm in fields such as natural language processing, implementation and evaluation of transfer learning approaches for chemistry are in the early stages. In this work, we demonstrate finetuning for downstream tasks on a graph neural network (GNN) trained over a molecular database containing 2.7 million water clusters. The use of Graphcore IPUs as an AI accelerator for training molecular GNNs reduces training time from a reported 2.7 days on 0.5M clusters to 92 minutes on 2.7M clusters. Finetuning the pretrained model for downstream tasks of molecular dynamics and level-of-theory transfer took only 8.3 hours and 28 minutes, respectively, on a single GPU.