26. A method of using a candidate selection system that includes at least a first trained machine learning system, the method comprising: receiving, by at least one processor-based system, a number of heuristic values each indicative of a strength of a respective pairing between two entities, the heuristic values generated by at least one machine learning system trained with at least a first entity and a second entity that are a historically successful pairing, and trained with a plurality of hypothetical alternative pairings between the first entity of the historically successful pairing and other entities of a first set of entities based at least in part on at least one value of each of at least one of a plurality of attributes associated with the second entity and based on at least one loosened constraint of a number of constraints applied to matches between the values of at least one of the attributes associated with the second entity; and executing, by the at least one processor-based system, a candidate selection algorithm which employs the received heuristic values and respective values for each of the plurality of attributes for each of a second set of entities to identify prospective candidates.
26. A method of using a candidate selection system that includes at least a first trained machine learning system, the method comprising: receiving, by at least one processor-based system, a number of heuristic values each indicative of a strength of a respective pairing between two entities, the heuristic values generated by at least one machine learning system trained with at least a first entity and a second entity that are a historically successful pairing, and trained with a plurality of hypothetical alternative pairings between the first entity of the historically successful pairing and other entities of a first set of entities based at least in part on at least one value of each of at least one of a plurality of attributes associated with the second entity and based on at least one loosened constraint of a number of constraints applied to matches between the values of at least one of the attributes associated with the second entity; and executing, by the at least one processor-based system, a candidate selection algorithm which employs the received heuristic values and respective values for each of the plurality of attributes for each of a second set of entities to identify prospective candidates. 31. The method of claim 26 wherein receiving the heuristic values includes receiving normalized heuristic values.