For each combination of model, query strategy and set of initially labelled training data points, we investigated the performance of up to 1500 training data points that had been labelled in total. Moreover, each combination was run five times to investigate the stochastic behaviours of the models and query strategies. Also, for each of the five different runs for a specific initial pool of labelled training data points, the same initial weights of the neural networks were used. Since training was cumulative, this means that the same initial weights were used to retrain the neural networks after each query.