Episodic learning is a popular practice among researchers and practitioners interested in few-shot learning. It consists of organising training in a series of learning problems, each relying on small support and query sets to mimic the few-shot circumstances encountered during evaluation. In this paper, we investigate the usefulness of episodic learning in Prototypical Networks, one the most popular algorithms making use of this practice. Surprisingly, in our experiments we found that episodic learning is detrimental to performance, and that it is under no circumstance beneficial to differentiate between a support and query set within a training batch. This non-episodic version of Prototypical Networks, which corresponds to the classic Neighbourhood Component Analysis, reliably improves over its episodic counterpart in multiple datasets, achieving an accuracy that is competitive with the state-of-the-art, despite being extremely simple.