We report on the systems that the Math Information Retrieval group at Masaryk University (MIRMU) and the team of Faculty of Informatics students (MSM) prepared for task 1 (find answers) of the ARQMath lab at the CLEF conference. To study the effects of different system settings and hyperparameters, we have prototyped several diverse math-aware information retrieval (MIR) systems: both “old” inverted index-based ones and new neural ones. By ensembling the results of the “weak” individual systems into committees, we report on entailments, benefits, and drawbacks of system ensembling. We evaluated the proposed individual systems and ensembles, considering their diversity, hyperparameters, and representations used, and classified their approaches. Our prototypes have helped to understand the challenging problems of question-answering in the stem domain: the key lies in the proper representation of document semantics. Our reproducible evaluation Python library PV211-utils allows to reproduce and further advance MIR re-search.