Despite recent advancements in table language models research, their real world application is still challenging. In industry, there is an abundance of tables found in spreadsheets, but acquisition of substantial amounts of labels is expensive, since only experts can annotate the often highly technical and domain-specific tables. Active learning could potentially reduce labeling costs, however, so far there are no works related to active learning in conjunction with table language models. In this paper we investigate different query strategies in a real-world industrial table language model use case. Our results show that there is potential for improvement and some fundamental questions to be addressed.