Graph Neural Networks (GNNs) have recently gained attention in the machine learning community. They have achieved state-of-the-art performance in a number of tasks by leveraging the geometric prior inherent to many real-world problems [1]. Concurrently, several model-agnostic algorithms for meta-learning have been developed, such as Model-Agnostic Meta-Learning (MAML) [2] and Reptile [3]. Although as their name suggests these algorithms are model agnostic, works in the literature have mainly applied them to classical fully-connected and convolutional neural networks. In this paper, we explore the application of Reptile to GNN regression tasks. We show that modelagnostic algorithms for meta-learning are also applicable to GNNs and specifically, that meta-learning can exploit the underlying structure of molecules to quickly adapt models to learning new molecular regression tasks. We experimentally demonstrate that GNN expressivity is correlated to metalearning performance. Finally, we also show that using GNN ensembles can even further improve meta-learning.