def rank_queries(molecules, ensemble, sort_order, options):
    """
    rank queries by value added to existing ensemble
    :param molecules:
    :param score_field:
    :param ensemble:
    :param sort_order:
    :param options:
    :return:
    """

    # generate query list
    query_list = [x for x in list(molecules[0].scores.keys()) if x not in ensemble]

    results = {}

    for query in query_list:
        es = EnsembleStorage() # an ensemble storage project

        # generate test_ensemble
        test_ensemble = ensemble[0:]
        test_ensemble.append(query)
        test_ensemble = tuple(test_ensemble)
        es.set_prop('ensemble', test_ensemble)

        # calculate its performance
        score_structure = classification.make_score_structure(molecules, test_ensemble)

        # determine auc value
        auc_structure = classification.make_auc_structure(score_structure)
        auc = classification.calculate_auc(auc_structure, sort_order, 'no stats')
        es.set_prop('auc', auc)

        # if the enrichment factor was set to anything other than 1, then we're training to maximize the corresponding
        # enrichment factor
        for fpf in classification.make_fpfList(options, score_structure):
            fpf = float(fpf)
            ef_structure = classification.make_ef_structure(score_structure, fpf, sort_order)
            if ef_structure:
                ef = classification.calculate_ef(ef_structure, fpf)
                es.set_prop(ef[0], ef[1], 'ef')

        # append results to metric list
        results[test_ensemble] = es

    # peel away the best performing ensemble
    best_ensemble = screener.find_best_ensemble(results, options)

    return list(best_ensemble)