  Active Learning (AL) methods have proven cost-saving against passive
supervised methods in many application domains. An active learner, aiming to
find some target hypothesis, formulates sequential queries to some oracle. The
set of hypotheses consistent with the already answered queries is called
version space. Several query selection measures (QSMs) for determining the best
query to ask next have been proposed. Assuming binaryoutcome queries, we
analyze various QSMs wrt. to the discrimination power of their selected queries
within the current version space. As a result, we derive superiority and
equivalence relations between these QSMs and introduce improved versions of
existing QSMs to overcome identified issues. The obtained picture gives a hint
about which QSMs should preferably be used in pool-based AL scenarios.
Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on
these, we demonstrate how efficient heuristic search methods for optimal
queries in query synthesis AL scenarios can be devised.
