We investigate whether multilingual image-text retrieval can be made practical without training a multi-modal model, using frozen multilingual uni-modal encoders and inducing cross-modal alignment through relative representations computed against a large external anchor set. We use English image-caption pairs as anchors and evaluate caption-to-image retrieval on 3,600 images with captions in 36 languages (some low-resource). Across multiple encoder pairings and hyperparameter settings, the method yields usable performance, positioning training-free relative representation as a baseline for multilingual cross-modal alignment when target-language multi-modal training data is unavailable.