Most organizational data in this world are stored as documents, and visual
retrieval plays a crucial role in unlocking the collective intelligence from
all these documents. However, existing benchmarks focus on English-only
document retrieval or only consider multilingual question-answering on a
single-page image. To bridge this gap, we introduce VisR-Bench, a multilingual
benchmark designed for question-driven multimodal retrieval in long documents.
Our benchmark comprises over 35K high-quality QA pairs across 1.2K documents,
enabling fine-grained evaluation of multimodal retrieval. VisR-Bench spans
sixteen languages with three question types (figures, text, and tables),
offering diverse linguistic and question coverage. Unlike prior datasets, we
include queries without explicit answers, preventing models from relying on
superficial keyword matching. We evaluate various retrieval models, including
text-based methods, multimodal encoders, and MLLMs, providing insights into
their strengths and limitations. Our results show that while MLLMs
significantly outperform text-based and multimodal encoder models, they still
struggle with structured tables and low-resource languages, highlighting key
challenges in multilingual visual retrieval.