  We study the classic problem of searching for a hidden target in the line and
the $m$-ray star, in a setting in which the searcher has some prediction on the
hider's position. We first focus on the main metric for comparing search
strategies under predictions; namely, we give positive and negative results on
the consistency-robustness tradeoff, where the performance of the strategy is
evaluated at extreme situations in which the prediction is either error-free,
or adversarially generated, respectively. For the line, we show tight bounds
concerning this tradeoff, under the untrusted advice model, in which the
prediction is in the form of a $k$-bit string which encodes the responses to
$k$ binary queries. For the star, we give tight, and near-tight tradeoffs in
the positional and the directional models, in which the prediction is related
to the position of the target within the star, and to the ray on which the
target hides, respectively. Last, for all three prediction models, we show how
to generalize our study to a setting in which the performance of the strategy
is evaluated as a function of the searcher's desired tolerance to prediction
errors, both in terms of positive and inapproximability results.
