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We consider the impact Active Learning (AL) has on effective and efficient text corpus annotation, and report on reduction rates for annotation efforts ranging up until 72%. We also address the issue whether a corpus annotated by means of AL–using a particular classifier and a particular feature set–can be re-used to train classifiers different from the ones employed by AL, supplying alternative feature sets as well. We, finally, report on our experience with the AL paradigm under real-world conditions, ie, the annotation of large-scale document corpora for the life sciences.