Cross-language retrieval of medical information needs to translate input queries into target language queries. It must be prepared to cope with 'new' words not yet listed in a multilingual lexicon. We address the issue of finding translational equivalents of such 'unknown' words from French to English in the medical domain. We rely on non-parallel, comparable corpora and an initial bilingual medical lexicon. We compare the distributional contexts of source and target words, testing several weighting factors and similarity measures. For the best combination (the Jaccard similarity measure with or without weighting), the correct translation is found in the top 10 candidates for more than 60% of the test words. This shows the potential of this technique to help extending bilingual medical lexicons.