We describe the participation of Dublin City University (DCU) in the FIRE-2014 transliteration search task (TST). The TST involves an ad-hoc search over a collection of Hindi film song lyrics. The Hindi language content of each document in the collection is either written in the native Devanagari script or transliterated in Roman script or a combination of both. The queries can be in mixed script as well. The task is challenging primarily because of the vocabulary mismatch which may arise due to the multiple transliteration alternatives. We attempt to address the vocabulary mismatch problem both during the indexing and retrieval stages. During indexing, we apply a rule-based normalization on some character sequences of the transliterated words in order to have a single representation in the index for the multiple transliteration alternatives. During the retrieval phase, we make use of prefix matched fuzzy query terms to account for the morphological variations of the transliterated words. The results show significant improvement over a standard baseline query likelihood language modelling (LM) approach. Additionally, we also apply statistical machine transliteration to train a transliteration model in order to predict the transliteration of out-of-vocabulary words. Surprisingly, even with satisfactory transliteration accuracy, we found that automatic transliteration of query terms degraded retrieval effectiveness.