Blending of search results from several vertical sources became standard among web search engines. Similar scenarios appear in computational advertising, news recommendation, and other interactive systems. As such environments give only partial feedback, the evaluation of new policies conventionally requires expensive online A/B tests. Counterfactual approach is a promising alternative, nevertheless, it requires specific conditions for a valid off-policy evaluation. We release a large-scale, real-world vertical-blending dataset gathered bySeznam.cz web search engine. The dataset contains logged partial feedback with the corresponding propensity and is thus suited for counterfactual evaluation. We provide basic checks for validity and evaluate several learning methods.