It is well known that the use of a good Machine Transliteration system improves the retrieval performance of Cross-Language Information Retrieval (CLIR) systems when the query and document languages have different orthography and phonetic alphabets. However, the effectiveness of a Machine Transliteration system in CLIR is limited by its ability to produce relevant transliterations, i.e. those transliterations which are actually present in the relevant documents. In this work, we propose a new approach to the problem of finding transliterations for out-of-vocabulary query terms. Instead of “generating” the transliterations using a Machine Transliteration system, we “mine” them, using a transliteration similarity model, from the top CLIR results for the query. We treat the query and each of the top results as “comparable” documents and search for transliterations in these comparable document pairs. We demonstrate the effectiveness of our approach using queries in two languages from two different linguistic families to retrieve English documents from two standard CLEF collections. We also compare our results with those of a state-of-the-art Machine Transliteration system.