Few-shot learning is a promising approach to molecular property prediction as supervised data is often very limited. However, many important molecular properties depend on complex molecular characteristics — such as the various 3D geometries a molecule may adopt or the types of chemical interactions it can form — that are not explicitly encoded in the feature space and must be approximated from limited data. Learning these characteristics can be difficult, especially for few-shot learning algorithms that are designed for fast adaptation to new tasks. In this work, we develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations, and a multi-task learning paradigm to structure the embedding space. The embeddings improve few-shot learning performance using Multi-Task, MAML, and Prototypical Networks on multiple molecular property prediction benchmarks.