In this paper, we explored different levels of textual representations for cross-lingual information retrieval. Beyond the traditional token level representation, we adopted the subword and character level representations for information retrieval that had shown to improve neural machine translation by reducing the out-of-vocabulary issues in machine translation. We found that crosslingual information retrieval performance can be improved by combining search results from subwords and token level representation.Additionally, we improved the search performance by combining and re-ranking the result sets from the different text representations for German, French and Japanese.