Cross-lingual text similarity provides an important measure to adjudge the contextual and semantic similarity between documents across different languages. Extraction of similar or aligned multilingual texts would enable efficient approaches for information retrieval and natural language processing applications. However, diversity of linguistic constructs coupled with domain specificity and low resources pose a significant challenge. In this paper, we present a study analyzing the performance of different existing approaches, and show that Word Mover’s Distance on aligned language embedding provides a reliable and cost-effective cross-lingual text similarity measure to tackle evolving domain information, even when compared to advanced machine learning models.