This work studies the effectiveness of query expansion for email search. Three state-of-the-art expansion methods are examined: 1) a global translation-based expansion model; 2) a personalized-based word embedding model; 3) the classical pseudo-relevance-feedback model. Experiments were conducted with two mail datasets extracted from a large query log of a Web mail service. Our results demonstrate the significant contribution of query expansion for measuring the similarity between the query and email messages. On the other hand, the contribution of expansion methods for a well trained learning-to-rank scoring function that exploits many relevance signals, was found to be modest.