To address the challenge of data scarcity in molecular property prediction tasks, Han Li et al. proposed MoTSE, a task similarity-enhanced transfer learning strategy designed to enhance molecular property prediction performance on datasets with limited labels. MoTSE involves pre-training a graph neural network on each task and feeding probe datasets into each pre-trained network. Subsequently, attribution-based and molecular representation similarity analyses are applied to define the distance between tasks. The most similar source task is then utilized to pre-train a network, which is subsequently fine-tuned on the target task. While the results reported for MoTSE are encouraging, it is worth noting that the method for defining distance is sensitive to both the choice of GNN architecture and the selection of the probe dataset.