  Binding precedents (S\'umulas Vinculantes) constitute a juridical instrument
unique to the Brazilian legal system and whose objectives include the
protection of the Federal Supreme Court against repetitive demands. Studies of
the effectiveness of these instruments in decreasing the Court's exposure to
similar cases, however, indicate that they tend to fail in such a direction,
with some of the binding precedents seemingly creating new demands. We
empirically assess the legal impact of five binding precedents, 11, 14, 17, 26
and 37, at the highest court level through their effects on the legal subjects
they address. This analysis is only possible through the comparison of the
Court's ruling about the precedents' themes before they are created, which
means that these decisions should be detected through techniques of Similar
Case Retrieval. The contributions of this article are therefore twofold: on the
mathematical side, we compare the uses of different methods of Natural Language
Processing -- TF-IDF, LSTM, BERT, and regex -- for Similar Case Retrieval,
whereas on the legal side, we contrast the inefficiency of these binding
precedents with a set of hypotheses that may justify their repeated usage. We
observe that the deep learning models performed significantly worse in the
specific Similar Case Retrieval task and that the reasons for binding
precedents to fail in responding to repetitive demand are heterogeneous and
case-dependent, making it impossible to single out a specific cause.
