On the other hand, we might encounter the following situation: we are only concerned about performance on a particular pool of data, -and we don't care about generalizability, -and data points can be effectively queried/accessed If all the conditions above are true, the learner can observe the test data and potentially exploit structures in their distribution.
there is really no difference between "unlabeled data" and "test data", and the research question is: "given some labeled data and a large set of (unlabeled) test data, can properties of the entire data set be used to make predictions?"