Transferring information retrieval (IR) models from a high-resource language (typically English) to other languages in a zero-shot fashion has become a widely adopted approach. In this work, we show that the effectiveness of zero-shot rankers diminishes when queries and documents are present in different languages. Motivated by this, we propose to train ranking models on artificially code-switched data instead, which we generate by utilizing bilingual lexicons. To this end, we experiment with lexicons induced from (1) cross-lingual word embeddings and (2) parallel Wikipedia page titles. We useResearch goal: What is the impact of artificially code-switched training data on the scalability of zero-shot cross-lingual retrieval performance when extending from distant language pairs to many-to-many language settings?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.