Text classification has extensive uses and deep learning has improved its performance using transformer language models.  A major hurdle for its use, however, is the paucity of labelled or annotated data. The data labelling process performed by domain experts is expensive and tedious to produce.  Active Learning is an approach to speeding up learning by judiciously selecting data to be annotated. Recently, advances in active learning theory have been made, but some experimental anomalies occur which need investigating.  Noteably, in BatchBALD (see references below) in Figure 4, it can be seen that batching data in small batches always beats a one-step lookahead technique.