1. A discovery system for inferentially screening a test entity to determine whether it exhibits a target property without directly measuring the test entity for the target property, the discovery system comprising: at least one processor and memory addressable by the at least one processor, the memory storing at least one program for execution by the at least one processor, the at least one program comprising instructions for: A) obtaining a training set that comprises a plurality of reference entities and, for each respective reference entity, (i) a respective measurement of each first feature in a respective subset of first features in an N-dimensional feature space and (ii) a respective measurement of each second feature in a respective subset of an M-dimensional feature space, wherein N is a positive integer of two or greater, M is a positive integer, the training set collectively provides at least one measurement for each first feature in the N-dimensional feature space, the training set collectively provides at least one measurement for each second feature in the M-dimensional feature space, at least one second feature in the M-dimensional feature space is a metric for the target property, the N-dimensional feature space does not include any of the second features in the M-dimensional space, the M-dimensional feature space does not include any of the first features in the N-dimensional space, the test entity comprises a protein, a fragment thereof, or a mixture of the protein with one or more other proteins, the obtaining (A) associates the test entity with a data structure comprising one or more extraction parameters used to extract the test entity from the test member, and the one or more extraction parameters comprises an extraction parameter in the group consisting of (i) an elution pH or time for the test entity, (ii) a buffer type used to extract the test entity from the test member, (iii) a specific pH or pH range used to extract the test entity from the test member, (iv) a specific ionic strength or an ionic strength range used to extract the test entity from the test member, and (v) a specific temperature or temperature range used to extract the test entity from the test member; B) identifying two or more first features, or one or more combinations thereof, in the N-dimensional feature space using a feature selection method and the training set, thereby selecting a set of first features {p 1 ,. .. , p N-K } from the N-dimensional feature space, wherein N−K is a positive integer less than N; C) training a model using measurements for the set of first features {p 1 ,. .. , p N-K } across the training set, thereby obtaining a trained model; D) obtaining measurement values for the set of first features {p 1 ,. .. , p N-K } of the test entity; E) inputting the set of first features {p 1 ,. .. , p N-K } of the test entity into the trained model thereby obtaining a trained model output value for the test entity; and F) comparing the trained model output value of the test entity to one or more trained model output values computed using measurement values for the set of first features {p 1 ,. .. , p N-K } of one or more reference entities that exhibits the target property thereby determining whether the test entity exhibits the target property.