The main approach of traditional information retrieval (IR) is to examine how many words from a query appear in a document. A drawback of this approach, however, is that it may fail to detect relevant documents where no or only few words from a query are found. The semantic analysis methods such as LSA (latent semantic analysis) and LDA (latent Dirichlet allocation) have been proposed to address the issue, but their performance is not superior compared to common IR approaches. Here we present a query-document similarity measure motivated by the Word Mover's Distance. Unlike other similarity measures, the proposed method relies on neural word embeddings to calculate the distance between words. Our method is efficient and straightforward to implement. The experimental results on TREC and PubMed show that our approach provides significantly better performance than BM25. We also discuss the pros and cons of our approach and show that there is a synergy effect when the word embedding measure is combined with the BM25 function.